AI-driven convolutional neural networks for accurate identification of yellow fever vectors

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Automated identification studies using Convolutional Neural Network (CNN) have been conducted for some urban mosquito vectors but not yet for sylvatic mosquito vectors that transmit the yellow fever and other arboviruses. Here, we evaluated the ability of the AlexNet CNN to identify four mosquito species: Aedes serratus , Aedes scapularis , Haemagogus leucocelaenus and Sabethes albiprivus and whether there is variation in AlexNet's ability to classify mosquitoes based on pictures of four different body regions. Methods The specimens were photographed using a cell phone connected to a stereoscope. Photographs were taken of the full-body, pronotum and lateral view of the thorax, which were pre-processed to train the AlexNet algorithm. The evaluation was based on the confusion matrix, the accuracy (10 pseudoreplicates) and the confidence interval for each experiment. Results Our study found that the AlexNet can accurately identify mosquito pictures of the genus Aedes , Sabethes and Haemagogus with over 90% accuracy. Furthermore, the algorithm performance did not change according to the body regions submitted. It is worth noting that the state of preservation of the mosquitoes, which were often damaged, may have affected the network's ability to differentiate between these species and thus accuracy rates could have been even higher. Conclusions Our results support the idea of applying CNNs for AI-driven identification of mosquito vectors of tropical diseases. This approach can potentially improve the surveillance of yellow fever vectors by health services and the population as well. Deep Learning Machine learning Culicidae Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Mosquito-borne diseases are a major public health concern. More than half of the world's population is exposed to arboviruses such as yellow fever, dengue, and Zika. Currently, 47 countries, including 34 in Africa and 13 in Central and South America, are endemic or have endemic regions for yellow fever [ 1 , 2 ]. These pathogens are mainly transmitted by mosquito bites, with Aedes aegypti and Aedes albopictus species being the most important urban vectors of these arboviruses [ 3 , 4 ]. Sylvatic mosquito species from the Sabethes and Haemagogus genera have been found to be naturally infected with the yellow fever virus and other arboviruses, for which no specific therapeutic agents exist [ 5 – 11 ]. One approach to prevent the spread of these diseases is by controlling the spread of the virus’ vector. Traditional mosquito surveillance relies on catches and species identification, which require regular manual inspection and dedicated personnel, making large-scale monitoring difficult and expensive. Additionally, identifying mosquitoes is a difficult task that demands specialized knowledge due to the vast range of morphological characteristics found in the Culicidae family, which includes all mosquitoes[ 12 ]. New approaches that rely on smartphones and the internet can enable new community and digital observatories with the task of species identification. These observatories allow individuals to submit photos of mosquitoes they come across. However, manually inspecting each picture is not a feasible long-term solution due to the large volume of pictures and professionals needed for the task [ 13 – 15 ]. Deep learning methods based on Convolutional Neural Networks (CNNs) have shown promise for mosquito identification [ 13 , 14 , 16 – 19 ]. CNNs simulate the human learning process for classifying pictures and extract important features from data automated without the need of human supervision[ 20 ]. AlexNet is a CNN that was pre-trained on 1.2 million images of objects, animals, and plants available in the ImageNet database. AlexNet has been successfully used for the identification of insect vectors [ 21 ]. Automated identification studies have been conducted for some urban mosquito vectors [ 13 , 14 , 16 – 19 ], but not yet for sylvatic mosquito vectors that transmit the yellow fever and other arboviruses [ 6 , 11 ]. Therefore, this study aimed to test the AlexNet’s ability in identifying Aedes serratus, Aedes scapularis, Haemagogus leucocelaenus , and Sabethes albiprivus. These four mosquito species were chosen because they are either confirmed vectors of the yellow fever virus ( Hg. leucocelaenus and Sa. albiprivus ) or suspected to be vectors of the virus ( Ae. serratus and Ae. scapularis ). We also asked whether there is variation in AlexNet's ability to classify mosquitoes based on pictures of four different body regions. Our findings indicate that the AlexNet network can accurately identify yellow fever vectors with over 90% accuracy for the four body regions analyzed. Methods To build the picture database, four mosquito species were selected. Among them, Sa. albiprivus (n = 100) and Ha. leucocelaenus (n = 98) were vectors of the wild yellow fever virus, while Aedes serratus (n = 100) and Aedes scapularis (n = 100) were vectors of other arboviruses. A total of 565 full-body pictures were captured, including 294 of the lateral thorax and 484 of the pronotum, resulting in 1,343 pictures available at the Laboratory of Medical Parasitology and Vector Biology at the University of Brasilia. The Culicidae specimens were mounted on a cardboard triangle using a pin. The mosquitoes were attached to the triangle by the thoracic pleura, leaving the legs facing the pin and the upper pleura free for observation. Pictures were captured using a cell phone camera (Samsung Galaxy S9) attached to a stereoscope (Zeiss Stemi 508). Photographs of the whole body, pronotum, and lateral view of the thorax were taken (Fig. 1 ), cropped into a square format, selecting only the mosquito, and resized to 227 x 227 pixels. They were then organized into folders based on the species and experiment to be conducted. The analyses were conducted using the MATLAB computer software and AlexNet machine learning algorithm which has been successfully used for automated identification of insect vectors [ 16 , 22 ]. The picture bank was divided into three parts: 80% for training, 10% for internal validation, and 10% for testing (Additional file 2: Database S1). The algorithm randomly selected the pictures for each stage. Four experiments were conducted with four classes, and ten pseudoreplicates were made for each. The first experiment used all the pictures, including total body, pronotum, and lateral thorax. The experiments used distinct pictures of each body part. The evaluation was based on the confusion matrix/heatmap and the mean and confidence interval of the accuracy for each experiment using the ‘Hmisc’ package in the computational software R 4.7.1, together with the RStudio 2023.03.1.446 interface [ 23 – 25 ]. Results Algorithm Performance The performance of the algorithm was evaluated by processing test pictures after training the AlexNet algorithm. Four experiments were conducted, in which each group (full-body, lateral thorax, and pronotum) was submitted to the AlexNet algorithm. Additionally, all 1,343 photos corresponding to the three groups were submitted together. The average confidence interval for accuracy was then calculated. The AlexNet algorithm demonstrated the highest accuracy in identifying mosquitoes when all pictures were analyzed together, providing the algorithm with the most comprehensive information about the mosquito's body. Each run was repeated 10 times, achieving an average accuracy of 94% (95% CI, 90–97). The run with the total body group achieved an average accuracy of 92.3% (95% CI, 83–97), while with the lateral view group the value was 93.7% (95% CI, 79–98). Finally, with the pronotum group, the average accuracy was 93.8% (95% CI, 83–98). The smallest confidence intervals were observed with pictures of all parts of the body (Fig. 2 ; see Additional file 1: Table S1 ). The algorithm's best performance was observed when photos from all three groups, were available. We then evaluated the algorithm's performance for each species based on these three groups of photos. The performance of the AlexNet algorithm in identifying mosquitoes of the genus Aedes was poor, with the highest confidence intervals observed for the two species in this genus, compared to the others (95% CI, 72–98) (Fig. 3 ). The algorithm misidentified at least one picture of Ae. scapularis as Ae. serratus or vice versa when presented with pictures of all three groups. In the run where all 1,343 pictures were available, Ae. serratus was misidentified as Hg. leucocelaenus . In the run with pictures of the lateral view, only mosquitoes of the genus Aedes were misidentified, with the two Aedes species being swapped. In the run with the pronotum, several misidentifications occurred. Ae. serratus was incorrectly identified as either Sa. albiprivus or Hg. leucocelaenus , while Ae. scapularis was identified as Sa. albiprivus (Fig. 4). It is worth noting that many of the photographed specimens were not well-preserved, which may have contributed to the high rate of misidentification. Discussion In this study, we aimed to identify four mosquito species that transmit yellow fever or other arboviruses by using a CNN (AlexNet). We also wanted to investigate whether the algorithm performance in classifying the mosquitoes changes according to the body regions shown on the pictures submitted. Our study found that the AlexNet can accurately identify mosquito pictures of the genus Aedes , Sabethes and Haemagogus with over 90% accuracy. Furthermore, the algorithm performance did not change according to the body regions shown. Lorenz et al.[ 26 ] classified mosquitoes based on morphometric characteristics of their wings using neural networks, achieving accuracies ranging from 86–100%. However, an identification system based only on a body structure such as the wing is more fragile because if the structure is not present in the analyzed photo, the identification is compromised. Therefore, a good identification system should work with any part of the insect's body. Sauer et al.[ 27 ] showed that best-performing CNN configuration achieved a precision of 99% to discriminate between Aedes and non- Aedes mosquito species; the mean precision to predict the Aedes species was 91% for RGB pictures. Motta et al.[ 16 ] used three pre-trained networks to identify urban mosquitoes ( Aedes and Culex ) achieving an accuracy of 76.2% for the GoogleNet, 52.4% for LeNet, and 51.2% for AlexNet. Okayasu et al.[ 28 ] showed better accuracy results (92.3%) with the identification of Ae. albopictus, Anopheles stefensi , and Cx. pipiens pallens using AlexNet based on data augmentation and 12,000 training pictures. More recently, Motta et al[ 17 ] optimized the CNN hyperparameters and obtained 97.3% accuracy in distinguishing between the mosquitoes of the genus Aedes and the Culex mosquitoes. Similarly, Goodwin et al.[ 29 ] and Park et al.[ 13 ] achieved 97% accuracy rates for mosquito identification ( Anopheles, Culex, Psorophora , and Aedes species) using deep learning neural networks. These networks rely on morphological features like those used by taxonomists [ 13 ]. Kittichai et al[ 15 ] using two YOLO v3 model identified Ae. aegypti , Ae. albopictus , and Cx. quinquefasciatus at a mean average accuracy of 98–100%. A recent study has shown that the accuracy and robustness of the CNN may reach 99% accuracy by incorporating spatial dropout layers to regularize the network and by modifying its structure to incorporate multi-view inputs [ 30 ]. Concatenating two YOLO v3 model exhibited the optimal performance in identifying mosquitoes, with mean average accuracy of 99%. The Swin MSI successfully identified 100% subspecieslevel in Culex pipiens Complex. Based on pictures of all body regions, AlexNet identified Ae. scapularis , Ae. serratus , Ha. leucocelaenus , and Sa.albiprivus at 94% accuracy on average (Fig. 2 ). Compared to previous studies that have used neural networks for mosquito identification, our accuracy rate of 94% is aligned with most results obtained by others. In our study, we did not find a significant difference in AlexNet performance in identifying mosquitoes based on different body regions. In fact, other studies have shown that CNNs are able to detect morphological differences in various body regions of Aedes mosquitoes[ 13 ], some of which are consistent with the most used dichotomous keys [ 31 ]. Such results reveal that deep learning models learn the distinctive morphological features of mosquitoes body areas; these are the same ones used by taxonomists. For instance, Aedes scapularis can be identified by its serrated abdomen, a proboscis that is larger than the anterior femur, and the mesonotum with white scales forming a circle. Aedes serratus is identified by its serrated abdomen, a proboscis that is similar to or smaller than the anterior femur, and a mesonotum that may or may not have a longitudinal stripe of white scales. These two species bear a striking resemblance to each other. Sabethes albiprivus has medium-sized legs with bluish scales, a golden-scaled abdomen that forms quadrilaterals, and a proboscis that is much smaller than the anterior femur. Sabethes albiprivus and Ha. leucocelaenus are two species with different morphological characteristics. Sa. albiprivus can be distinguished from Ha. leucocelaenus by its predominantly dull, dark color and pleura with two vertical lines of silvery scales[ 7 ]. Other studies show that the accuracy of CNNs in identifying other insects is not significantly affected by the body region shown on the picture [ 22 ]. Our findings show that the morphological characteristics used for the identification of the mosquitoes included in this study are present in multiple regions of the body and therefore any of the body regions here studied allowed the AlexNet to accurately identify the mosquito species. Deep learning neural networks consist of multiple convolutional layers, and databases with more pictures are more conducive to learning [ 21 ]. Additionally, many studies indicate that a larger picture bank improves the algorithm's performance [ 13 , 14 , 16 , 17 , 22 , 32 , 33 ]. Even though a database with thousands of pictures is always desired, using a database with only 1,343 pictures, we reached accuracy rates similar to those using databases 10x bigger than ours [ 17 , 28 ] AlexNet accuracy to identify Sabethes and Haemogogus mosquitoes was similar to the accuracy obtained with other CNNs used to identify other genera [ 17 , 28 ]. However, the accuracy of the AlexNet in identifying Ae. serratus and Ae. scapularis was below 90% and thus suboptimal when compared with the performance of other CNNs (VGG-16, ResNet-50, SqueezeNet) that apply data augmentation and fine-tuning techniques to identify Ae. aegypti [ 16 , 17 ], Ae. albopictus and Ae. vexans [ 13 ]. These accuracy values may be due to differences in algorithm training [ 13 ]. For instance, optimization of CNN hyperparameters increase the accuracy of mosquito identification [ 17 ]. The poor performance of the algorithm in some cases may have been influenced by the state of preservation of the specimens. Analysis of the misidentified pictures in all experiments showed that the photographed specimens were not well preserved, especially in the pronotum area, where bristles and scales were missing, as well as the legs. Due to their size and the presence of scales and bristles, mosquitoes are easily damaged during capture, freezing, and drying, resulting in the loss of critical morphological features necessary for proper identification. The state of preservation of the mosquitoes was a limiting factor in this work, and good preservation of specimens is important for optimal algorithm performance. In this study we successfully identified four species of mosquitoes that transmit yellow fever or other arboviruses using AlexNet. Our results support the idea of applying CNNs to the AI-driven identification of mosquito vectors of tropical diseases. This approach can potentially improve the surveillance of yellow fever vectors by health services and the population as well. Additional studies applying algorithms that identify mosquitoes may clarify which visual information is most relevant for the AI-driven identification of mosquito species. Conclusions We aimed to identify four mosquito species that transmit yellow fever and other arboviruses using AlexNet. We found that the AlexNet CNN can identify mosquito pictures of the genus Aedes , Sabethes and Haemagogus with over 90% accuracy, regardless of the body region being shown. Our results support the idea of applying CNNs for AI-driven identification of mosquito vectors of tropical diseases. This approach can potentially improve the surveillance of yellow fever vectors by health services and the population as well. Abbreviations CI: confidence interval; CNN: Convolutional Neural Network. Declarations Acknowledgments We thank Marcia Triunfol at Publicase for reviewing this manuscript. Authors’ contributions TOA, RG-G, and VLM conceived the study. RG-G raised funds and administered the project. RG-G, and VLM contributed to the design of trial methods. TOA, RG-G, and VLM performed research. RG-G supervised students involved in laboratory research. TOA obtained the databases. RG-G, and VLM curated the dataset and analyzed the data. RG-G and TOA drafted the first version of the manuscript. All authors contributed to the interpretation of results, read, and commented on manuscript drafts, and approved the final version. Funding TOA received specific funding of Coordenação de Aperfeiçoamento de Pessoal de Nível Superior https://www.capes.gov.br/ Award Number: finance code 001. RGG received specific funding of Conselho Nacional de Desenvolvimento Científico e Tecnológico http://www.cnpq.br/. The funding sources of this study had no role in the study design, data collection, data analysis, data interpretation, writing of the report, or in the decision to submit the paper for publication. Availability of data and materials Data supporting the conclusions of this article are included in the article and its additional files. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References World Health Organization. World Health Day 2014: Preventing vector-borne diseases. Geneva: World Health Organization; 2014. Available from: https://www.who.int/news/item/02-04-2014-world-health-day-2014-preventing-vector-borne-diseases. Accessed May 16, 2024. World Health Organization. Yellow fever. Geneva: World Health Organization; 2023. Available from: https://www.who.int/news-room/fact-sheets/detail/yellow-fever. Accessed Mar 03,2024. 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Gurgel-Gonçalves R, Komp E, Campbell LP, Khalighifar A, Mellenbruch J, Mendonça VJ, et al. Automated identification of insect vectors of Chagas disease in Brazil and Mexico: The Virtual Vector Lab. PeerJ. 2017;5:e3040. Khalighifar A, Komp E, Ramsey JM, Gurgel-Gonçalves R, Peterson AT. Deep Learning Algorithms Improve Automated Identification of Chagas Disease Vectors. J Med Entomol. 2019;56(2):461-468. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1TableS1observedaccuracy.docx Additional file 1: Table S1. Accuracy observed for each experiment Additionalfile1Database.xlsx Additional file 2: Database S1 Cite Share Download PDF Status: Published Journal Publication published 02 Aug, 2024 Read the published version in Parasites & Vectors → Version 1 posted Editorial decision: Revision requested 21 Jun, 2024 Reviews received at journal 19 Jun, 2024 Reviews received at journal 01 Jun, 2024 Reviewers agreed at journal 29 May, 2024 Reviewers agreed at journal 28 May, 2024 Reviewers agreed at journal 27 May, 2024 Reviewers invited by journal 26 May, 2024 Editor assigned by journal 25 May, 2024 Submission checks completed at journal 25 May, 2024 First submitted to journal 24 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4473317","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":309645749,"identity":"a70043a9-fa6a-4782-89ab-558725d1ea6b","order_by":0,"name":"Taís Oliveira Araújo","email":"","orcid":"","institution":"Programa de Pós-Graduação em Medicina Tropical, Universidade de Brasília","correspondingAuthor":false,"prefix":"","firstName":"Taís","middleName":"Oliveira","lastName":"Araújo","suffix":""},{"id":309645750,"identity":"49cdfb6e-55db-4047-9821-a267de90efbe","order_by":1,"name":"Vinicius Lima Miranda","email":"","orcid":"","institution":"Universidade de Brasília","correspondingAuthor":false,"prefix":"","firstName":"Vinicius","middleName":"Lima","lastName":"Miranda","suffix":""},{"id":309645751,"identity":"a6bc77ff-e381-460a-8ef7-c2f62312ec79","order_by":2,"name":"Rodrigo Gurgel-Gonçalves","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYNCCAgYGfgkwS0KGSC0GDAySMxgYG4BaeIjXYnADrIWBsBb5iNzHLz4Y3LPbfLv5+KMbNRY8DOyHj27Ap8XwRrqZ5QyD4uRtd44lNuccAzqMJy3tBl4tM9LYjHkMEpLNbuQYNuewAbVI8JgR1vIHqMV4BkjLPyK0yEukMT9mMEiwM5AAasltI0KLAc8zNsYeg4QEiRtpibNz+yR42Aj5Rb49jfnDj4oEe/4ZyQc+53yrk+NnP3wMvy0HGNhA8Z7YABNhw6ccbEsDA/MHIG1PSOEoGAWjYBSMYAAAH1JFhe0ZebMAAAAASUVORK5CYII=","orcid":"","institution":"Programa de Pós-Graduação em Medicina Tropical, Universidade de Brasília","correspondingAuthor":true,"prefix":"","firstName":"Rodrigo","middleName":"","lastName":"Gurgel-Gonçalves","suffix":""}],"badges":[],"createdAt":"2024-05-24 15:21:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4473317/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4473317/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13071-024-06406-2","type":"published","date":"2024-08-02T15:57:24+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":57863051,"identity":"52fb42fb-8f7c-44bf-af14-149e1d6dc8f6","added_by":"auto","created_at":"2024-06-06 15:07:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":822491,"visible":true,"origin":"","legend":"\u003cp\u003eExamples of pictures used to train the AlexNet network. Mosquitoes photographed with a stereomicroscope and cell phone: (A\u003cem\u003e) Aedes scapularis\u003c/em\u003e, (B) \u003cem\u003eAedes serratus\u003c/em\u003e, (C) \u003cem\u003eHaemagogus leucocelaenus,\u003c/em\u003e (D) \u003cem\u003eSabethes albiprivus\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig1.Mosquitoimages.png","url":"https://assets-eu.researchsquare.com/files/rs-4473317/v1/27db87bdd781e67875fa0e94.png"},{"id":57863048,"identity":"82f8275b-9031-4a13-8f1a-1fba4ab4a28e","added_by":"auto","created_at":"2024-06-06 15:07:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":105266,"visible":true,"origin":"","legend":"\u003cp\u003eAverage accuracy of the AlexNet algorithm in identifying mosquito vectors of yellow fever and other arboviruses. The experiments took into account identification from images of isolated body parts regions, with 95% confidence intervals. Each run was replicated 10x.\u003c/p\u003e","description":"","filename":"Fig2.Plotaccuracy..png","url":"https://assets-eu.researchsquare.com/files/rs-4473317/v1/dc1479ad1fc3cecddcb78940.png"},{"id":57863053,"identity":"a45f43e6-46fa-43c7-9092-9885e97ea629","added_by":"auto","created_at":"2024-06-06 15:07:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":364394,"visible":true,"origin":"","legend":"\u003cp\u003eAverage accuracy of the AlexNet algorithm in interspecies identification. The experiments took into account identification from images of isolated body parts regions, with 95% confidence intervals. Each run was replicated 10x.\u003c/p\u003e","description":"","filename":"Fig3.Confusionmatrices.png","url":"https://assets-eu.researchsquare.com/files/rs-4473317/v1/b85528a167455b914622089c.png"},{"id":57863050,"identity":"00644458-b946-4a0e-96eb-ffa8ca7a75e5","added_by":"auto","created_at":"2024-06-06 15:07:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":99516,"visible":true,"origin":"","legend":"\u003cp\u003eConfusion matrices showing the classification hits and misses of the 10% of the test images for the four experiments. Each run was replicated 10x.\u003c/p\u003e","description":"","filename":"Fig4.Plotallexp.png","url":"https://assets-eu.researchsquare.com/files/rs-4473317/v1/9c31d258a1ce1143620725d2.png"},{"id":61793396,"identity":"491bdc5e-ab79-419b-bc7f-0280cebb094a","added_by":"auto","created_at":"2024-08-05 16:11:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2197799,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4473317/v1/2c27f753-1cc7-43a1-8da6-3a1a2141491c.pdf"},{"id":57863272,"identity":"635997b7-ea13-4b26-b3d2-4b42d9e5060e","added_by":"auto","created_at":"2024-06-06 15:15:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":57037,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 1: Table S1. \u003c/strong\u003eAccuracy observed for each experiment\u003c/p\u003e","description":"","filename":"Additionalfile1TableS1observedaccuracy.docx","url":"https://assets-eu.researchsquare.com/files/rs-4473317/v1/e6458b218d132a07152f5b03.docx"},{"id":57863054,"identity":"31802c14-f931-40f2-baa5-d524b9bfb5d4","added_by":"auto","created_at":"2024-06-06 15:07:03","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":173870,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 2: Database S1\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Additionalfile1Database.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4473317/v1/a92e291e146c70e9e2dd758a.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"AI-driven convolutional neural networks for accurate identification of yellow fever vectors","fulltext":[{"header":"Background","content":"\u003cp\u003eMosquito-borne diseases are a major public health concern. More than half of the world's population is exposed to arboviruses such as yellow fever, dengue, and Zika. Currently, 47 countries, including 34 in Africa and 13 in Central and South America, are endemic or have endemic regions for yellow fever [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These pathogens are mainly transmitted by mosquito bites, with \u003cem\u003eAedes aegypti\u003c/em\u003e and \u003cem\u003eAedes albopictus\u003c/em\u003e species being the most important urban vectors of these arboviruses [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Sylvatic mosquito species from the \u003cem\u003eSabethes\u003c/em\u003e and \u003cem\u003eHaemagogus\u003c/em\u003e genera have been found to be naturally infected with the yellow fever virus and other arboviruses, for which no specific therapeutic agents exist [\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9 CR10\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne approach to prevent the spread of these diseases is by controlling the spread of the virus\u0026rsquo; vector. Traditional mosquito surveillance relies on catches and species identification, which require regular manual inspection and dedicated personnel, making large-scale monitoring difficult and expensive. Additionally, identifying mosquitoes is a difficult task that demands specialized knowledge due to the vast range of morphological characteristics found in the Culicidae family, which includes all mosquitoes[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. New approaches that rely on smartphones and the internet can enable new community and digital observatories with the task of species identification. These observatories allow individuals to submit photos of mosquitoes they come across. However, manually inspecting each picture is not a feasible long-term solution due to the large volume of pictures and professionals needed for the task [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDeep learning methods based on Convolutional Neural Networks (CNNs) have shown promise for mosquito identification [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. CNNs simulate the human learning process for classifying pictures and extract important features from data automated without the need of human supervision[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. AlexNet is a CNN that was pre-trained on 1.2\u0026nbsp;million images of objects, animals, and plants available in the ImageNet database. AlexNet has been successfully used for the identification of insect vectors [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Automated identification studies have been conducted for some urban mosquito vectors [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], but not yet for sylvatic mosquito vectors that transmit the yellow fever and other arboviruses [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, this study aimed to test the AlexNet\u0026rsquo;s ability in identifying \u003cem\u003eAedes serratus, Aedes scapularis, Haemagogus leucocelaenus\u003c/em\u003e, and \u003cem\u003eSabethes albiprivus.\u003c/em\u003e These four mosquito species were chosen because they are either confirmed vectors of the yellow fever virus (\u003cem\u003eHg. leucocelaenus\u003c/em\u003e and \u003cem\u003eSa. albiprivus\u003c/em\u003e) or suspected to be vectors of the virus (\u003cem\u003eAe. serratus\u003c/em\u003e and \u003cem\u003eAe. scapularis\u003c/em\u003e). We also asked whether there is variation in AlexNet's ability to classify mosquitoes based on pictures of four different body regions. Our findings indicate that the AlexNet network can accurately identify yellow fever vectors with over 90% accuracy for the four body regions analyzed.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eTo build the picture database, four mosquito species were selected. Among them, \u003cem\u003eSa. albiprivus\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;100) and \u003cem\u003eHa. leucocelaenus\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;98) were vectors of the wild yellow fever virus, while \u003cem\u003eAedes serratus\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;100) and \u003cem\u003eAedes scapularis\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;100) were vectors of other arboviruses. A total of 565 full-body pictures were captured, including 294 of the lateral thorax and 484 of the pronotum, resulting in 1,343 pictures available at the Laboratory of Medical Parasitology and Vector Biology at the University of Brasilia. The Culicidae specimens were mounted on a cardboard triangle using a pin. The mosquitoes were attached to the triangle by the thoracic pleura, leaving the legs facing the pin and the upper pleura free for observation. Pictures were captured using a cell phone camera (Samsung Galaxy S9) attached to a stereoscope (Zeiss Stemi 508). Photographs of the whole body, pronotum, and lateral view of the thorax were taken (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), cropped into a square format, selecting only the mosquito, and resized to 227 x 227 pixels. They were then organized into folders based on the species and experiment to be conducted.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe analyses were conducted using the MATLAB computer software and AlexNet machine learning algorithm which has been successfully used for automated identification of insect vectors [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The picture bank was divided into three parts: 80% for training, 10% for internal validation, and 10% for testing (Additional file 2: Database S1). The algorithm randomly selected the pictures for each stage. Four experiments were conducted with four classes, and ten pseudoreplicates were made for each. The first experiment used all the pictures, including total body, pronotum, and lateral thorax. The experiments used distinct pictures of each body part. The evaluation was based on the confusion matrix/heatmap and the mean and confidence interval of the accuracy for each experiment using the \u0026lsquo;Hmisc\u0026rsquo; package in the computational software R 4.7.1, together with the RStudio 2023.03.1.446 interface [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eAlgorithm Performance\u003c/h2\u003e \u003cp\u003eThe performance of the algorithm was evaluated by processing test pictures after training the AlexNet algorithm. Four experiments were conducted, in which each group (full-body, lateral thorax, and pronotum) was submitted to the AlexNet algorithm. Additionally, all 1,343 photos corresponding to the three groups were submitted together. The average confidence interval for accuracy was then calculated. The AlexNet algorithm demonstrated the highest accuracy in identifying mosquitoes when all pictures were analyzed together, providing the algorithm with the most comprehensive information about the mosquito's body. Each run was repeated 10 times, achieving an average accuracy of 94% (95% CI, 90\u0026ndash;97). The run with the total body group achieved an average accuracy of 92.3% (95% CI, 83\u0026ndash;97), while with the lateral view group the value was 93.7% (95% CI, 79\u0026ndash;98). Finally, with the pronotum group, the average accuracy was 93.8% (95% CI, 83\u0026ndash;98). The smallest confidence intervals were observed with pictures of all parts of the body (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; see Additional file 1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe algorithm's best performance was observed when photos from all three groups, were available. We then evaluated the algorithm's performance for each species based on these three groups of photos. The performance of the AlexNet algorithm in identifying mosquitoes of the genus \u003cem\u003eAedes\u003c/em\u003e was poor, with the highest confidence intervals observed for the two species in this genus, compared to the others (95% CI, 72\u0026ndash;98) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The algorithm misidentified at least one picture of \u003cem\u003eAe. scapularis\u003c/em\u003e as \u003cem\u003eAe. serratus\u003c/em\u003e or vice versa when presented with pictures of all three groups. In the run where all 1,343 pictures were available, \u003cem\u003eAe. serratus\u003c/em\u003e was misidentified as \u003cem\u003eHg. leucocelaenus\u003c/em\u003e. In the run with pictures of the lateral view, only mosquitoes of the genus \u003cem\u003eAedes\u003c/em\u003e were misidentified, with the two \u003cem\u003eAedes\u003c/em\u003e species being swapped. In the run with the pronotum, several misidentifications occurred. \u003cem\u003eAe. serratus\u003c/em\u003e was incorrectly identified as either \u003cem\u003eSa. albiprivus\u003c/em\u003e or \u003cem\u003eHg. leucocelaenus\u003c/em\u003e, while \u003cem\u003eAe. scapularis\u003c/em\u003e was identified as \u003cem\u003eSa. albiprivus\u003c/em\u003e (Fig.\u0026nbsp;4). It is worth noting that many of the photographed specimens were not well-preserved, which may have contributed to the high rate of misidentification.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we aimed to identify four mosquito species that transmit yellow fever or other arboviruses by using a CNN (AlexNet). We also wanted to investigate whether the algorithm performance in classifying the mosquitoes changes according to the body regions shown on the pictures submitted. Our study found that the AlexNet can accurately identify mosquito pictures of the genus \u003cem\u003eAedes\u003c/em\u003e, \u003cem\u003eSabethes\u003c/em\u003e and \u003cem\u003eHaemagogus\u003c/em\u003e with over 90% accuracy. Furthermore, the algorithm performance did not change according to the body regions shown.\u003c/p\u003e \u003cp\u003eLorenz et al.[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] classified mosquitoes based on morphometric characteristics of their wings using neural networks, achieving accuracies ranging from 86\u0026ndash;100%. However, an identification system based only on a body structure such as the wing is more fragile because if the structure is not present in the analyzed photo, the identification is compromised. Therefore, a good identification system should work with any part of the insect's body. Sauer et al.[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] showed that best-performing CNN configuration achieved a precision of 99% to discriminate between \u003cem\u003eAedes\u003c/em\u003e and non-\u003cem\u003eAedes\u003c/em\u003e mosquito species; the mean precision to predict the \u003cem\u003eAedes\u003c/em\u003e species was 91% for RGB pictures. Motta et al.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] used three pre-trained networks to identify urban mosquitoes (\u003cem\u003eAedes\u003c/em\u003e and \u003cem\u003eCulex\u003c/em\u003e) achieving an accuracy of 76.2% for the GoogleNet, 52.4% for LeNet, and 51.2% for AlexNet. Okayasu et al.[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] showed better accuracy results (92.3%) with the identification of \u003cem\u003eAe. albopictus, Anopheles stefensi\u003c/em\u003e, and \u003cem\u003eCx. pipiens pallens\u003c/em\u003e using AlexNet based on data augmentation and 12,000 training pictures. More recently, Motta et al[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] optimized the CNN hyperparameters and obtained 97.3% accuracy in distinguishing between the mosquitoes of the genus \u003cem\u003eAedes\u003c/em\u003e and the \u003cem\u003eCulex\u003c/em\u003e mosquitoes. Similarly, Goodwin et al.[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and Park et al.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] achieved 97% accuracy rates for mosquito identification (\u003cem\u003eAnopheles, Culex, Psorophora\u003c/em\u003e, and \u003cem\u003eAedes\u003c/em\u003e species) using deep learning neural networks. These networks rely on morphological features like those used by taxonomists [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Kittichai et al[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] using two YOLO v3 model identified \u003cem\u003eAe. aegypti\u003c/em\u003e, \u003cem\u003eAe. albopictus\u003c/em\u003e, and \u003cem\u003eCx. quinquefasciatus\u003c/em\u003e at a mean average accuracy of 98\u0026ndash;100%. A recent study has shown that the accuracy and robustness of the CNN may reach 99% accuracy by incorporating spatial dropout layers to regularize the network and by modifying its structure to incorporate multi-view inputs [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Concatenating two YOLO v3 model exhibited the optimal performance in identifying mosquitoes, with mean average accuracy of 99%. The Swin MSI successfully identified 100% subspecieslevel in \u003cem\u003eCulex pipiens\u003c/em\u003e Complex. Based on pictures of all body regions, AlexNet identified \u003cem\u003eAe. scapularis\u003c/em\u003e, \u003cem\u003eAe. serratus\u003c/em\u003e, \u003cem\u003eHa. leucocelaenus\u003c/em\u003e, and \u003cem\u003eSa.albiprivus\u003c/em\u003e at 94% accuracy on average (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Compared to previous studies that have used neural networks for mosquito identification, our accuracy rate of 94% is aligned with most results obtained by others.\u003c/p\u003e \u003cp\u003eIn our study, we did not find a significant difference in AlexNet performance in identifying mosquitoes based on different body regions. In fact, other studies have shown that CNNs are able to detect morphological differences in various body regions of \u003cem\u003eAedes\u003c/em\u003e mosquitoes[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], some of which are consistent with the most used dichotomous keys [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Such results reveal that deep learning models learn the distinctive morphological features of mosquitoes body areas; these are the same ones used by taxonomists. For instance, \u003cem\u003eAedes scapularis\u003c/em\u003e can be identified by its serrated abdomen, a proboscis that is larger than the anterior femur, and the mesonotum with white scales forming a circle. \u003cem\u003eAedes serratus\u003c/em\u003e is identified by its serrated abdomen, a proboscis that is similar to or smaller than the anterior femur, and a mesonotum that may or may not have a longitudinal stripe of white scales. These two species bear a striking resemblance to each other. \u003cem\u003eSabethes albiprivus\u003c/em\u003e has medium-sized legs with bluish scales, a golden-scaled abdomen that forms quadrilaterals, and a proboscis that is much smaller than the anterior femur. \u003cem\u003eSabethes albiprivus\u003c/em\u003e and \u003cem\u003eHa. leucocelaenus\u003c/em\u003e are two species with different morphological characteristics. \u003cem\u003eSa. albiprivus\u003c/em\u003e can be distinguished from \u003cem\u003eHa. leucocelaenus\u003c/em\u003e by its predominantly dull, dark color and pleura with two vertical lines of silvery scales[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Other studies show that the accuracy of CNNs in identifying other insects is not significantly affected by the body region shown on the picture [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Our findings show that the morphological characteristics used for the identification of the mosquitoes included in this study are present in multiple regions of the body and therefore any of the body regions here studied allowed the AlexNet to accurately identify the mosquito species.\u003c/p\u003e \u003cp\u003eDeep learning neural networks consist of multiple convolutional layers, and databases with more pictures are more conducive to learning [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Additionally, many studies indicate that a larger picture bank improves the algorithm's performance [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Even though a database with thousands of pictures is always desired, using a database with only 1,343 pictures, we reached accuracy rates similar to those using databases 10x bigger than ours [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] AlexNet accuracy to identify \u003cem\u003eSabethes\u003c/em\u003e and \u003cem\u003eHaemogogus\u003c/em\u003e mosquitoes was similar to the accuracy obtained with other CNNs used to identify other genera [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, the accuracy of the AlexNet in identifying \u003cem\u003eAe. serratus\u003c/em\u003e and \u003cem\u003eAe. scapularis\u003c/em\u003e was below 90% and thus suboptimal when compared with the performance of other CNNs (VGG-16, ResNet-50, SqueezeNet) that apply data augmentation and fine-tuning techniques to identify \u003cem\u003eAe. aegypti\u003c/em\u003e [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], \u003cem\u003eAe. albopictus\u003c/em\u003e and \u003cem\u003eAe. vexans\u003c/em\u003e [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese accuracy values may be due to differences in algorithm training [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. For instance, optimization of CNN hyperparameters increase the accuracy of mosquito identification [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The poor performance of the algorithm in some cases may have been influenced by the state of preservation of the specimens. Analysis of the misidentified pictures in all experiments showed that the photographed specimens were not well preserved, especially in the pronotum area, where bristles and scales were missing, as well as the legs. Due to their size and the presence of scales and bristles, mosquitoes are easily damaged during capture, freezing, and drying, resulting in the loss of critical morphological features necessary for proper identification. The state of preservation of the mosquitoes was a limiting factor in this work, and good preservation of specimens is important for optimal algorithm performance.\u003c/p\u003e \u003cp\u003eIn this study we successfully identified four species of mosquitoes that transmit yellow fever or other arboviruses using AlexNet. Our results support the idea of applying CNNs to the AI-driven identification of mosquito vectors of tropical diseases. This approach can potentially improve the surveillance of yellow fever vectors by health services and the population as well. Additional studies applying algorithms that identify mosquitoes may clarify which visual information is most relevant for the AI-driven identification of mosquito species.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe aimed to identify four mosquito species that transmit yellow fever and other arboviruses using AlexNet. We found that the AlexNet CNN can identify mosquito pictures of the genus \u003cem\u003eAedes\u003c/em\u003e, \u003cem\u003eSabethes\u003c/em\u003e and \u003cem\u003eHaemagogus\u003c/em\u003e with over 90% accuracy, regardless of the body region being shown. Our results support the idea of applying CNNs for AI-driven identification of mosquito vectors of tropical diseases. This approach can potentially improve the surveillance of yellow fever vectors by health services and the population as well.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCI: confidence interval; CNN: Convolutional Neural Network.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Marcia Triunfol at Publicase for reviewing this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTOA, RG-G, and VLM conceived the study. RG-G raised funds and administered the project. RG-G, and VLM contributed to the design of trial methods. TOA, RG-G, and VLM performed research. RG-G supervised students involved in laboratory research. TOA obtained the databases. RG-G, and VLM curated the dataset and analyzed the data. RG-G and TOA drafted the first version of the manuscript. All authors contributed to the interpretation of results, read, and commented on manuscript drafts, and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTOA received specific funding of Coordena\u0026ccedil;\u0026atilde;o de Aperfei\u0026ccedil;oamento de Pessoal de N\u0026iacute;vel Superior https://www.capes.gov.br/ Award Number: finance code 001. RGG received specific funding of Conselho Nacional de Desenvolvimento Cient\u0026iacute;fico e Tecnol\u0026oacute;gico http://www.cnpq.br/.\u0026nbsp;The funding sources of this study had no role in the study design, data collection, data analysis, data interpretation, writing of the report, or in the decision to submit the paper for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData supporting the conclusions of this article are included in the article and its additional files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. World Health Day 2014: Preventing vector-borne diseases. Geneva: World Health Organization; 2014. Available from: https://www.who.int/news/item/02-04-2014-world-health-day-2014-preventing-vector-borne-diseases. Accessed May 16, 2024.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Yellow fever. Geneva: World Health Organization; 2023. Available from: https://www.who.int/news-room/fact-sheets/detail/yellow-fever. Accessed Mar 03,2024.\u003c/li\u003e\n\u003cli\u003eDonalisio MR, Freitas ARR, Zuben APB Von. Arboviruses emerging in Brazil: challenges for clinic and implications for public health. Rev Saude Publica. 2017;51.\u003c/li\u003e\n\u003cli\u003eAhebwa A, Hii J, Neoh KB, Chareonviriyaphap T. \u003cem\u003eAedes aegypti\u003c/em\u003e and \u003cem\u003eAe. albopictus\u003c/em\u003e (Diptera: Culicidae) ecology, biology, behaviour, and implications on arbovirus transmission in Thailand: Review. 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Yellow fever in Par\u0026aacute; State, Amazon region of Brazil, 1998-1999: Entomologic and epidemiologic findings. Emerg Infect Dis. 2001;7.\u003c/li\u003e\n\u003cli\u003eVasconcelos PFC, Rodrigues SG, Degallier N, Moraes MAP, Travassos Da Rosa JFS, Travassos Da Rosa ES, et al. An epidemic of sylvatic yellow fever in the southeast region of Maranhao State, Brazil, 1993-1994: Epidemiologic and entomologic findings. Am J Trop Med Hyg. 1997;57.\u003c/li\u003e\n\u003cli\u003eVasconcelos PFC, Travassos da Rosa APA, Pinheiro FP, Shope RE, Travassos da Rosa JFS, Rodrigues SG, et al. Arboviruses pathogenic for man in Brazil. An overview of Arbovirology in Brazil and neighbouring countries. 1998. \u003c/li\u003e\n\u003cli\u003eCano ME, Marti GA, Alencar J, Silva SOF, Micieli MV. Categorization by Score of Mosquito Species (Diptera: Culicidae) Related to Yellow Fever Epizootics in Argentina. J Med Entomol. 2022;59(5):1384-1388.\u003c/li\u003e\n\u003cli\u003eWilkerson RC, Linton YM, Fonseca DM, Schultz TR, Price DC, Strickman DA. Making mosquito taxonomy useful: A stable classification of tribe Aedini that balances utility with current knowledge of evolutionary relationships. PLoS One. 2015;10(3):e0133602.\u003c/li\u003e\n\u003cli\u003ePark J, Kim DI, Choi B, Kang W, Kwon HW. Classification and Morphological Analysis of Vector Mosquitoes using Deep Convolutional Neural Networks. Sci Rep. 2020;10(1):3144.\u003c/li\u003e\n\u003cli\u003ePataki BA, Garriga J, Eritja R, Palmer JRB, Bartumeus F, Csabai I. Deep learning identification for citizen science surveillance of tiger mosquitoes. Sci Rep. 2021;11(1):206.\u003c/li\u003e\n\u003cli\u003eKittichai V, Pengsakul T, Chumchuen K, Samung Y, Sriwichai P, Phatthamolrat N, et al. Deep learning approaches for challenging species and gender identification of mosquito vectors. Sci Rep. 2021;11(1):19375.\u003c/li\u003e\n\u003cli\u003eMotta D, Santos A\u0026Aacute;B, Winkler I, Machado BAS, Pereira DADI, Cavalcanti AM, et al. Application of convolutional neural networks for classification of adult mosquitoes in the field. PLoS One. 2019;14(5):e0216427.\u003c/li\u003e\n\u003cli\u003eMotta D, Bandeira Santos A\u0026Aacute;, Souza Machado BA, Vicente Ribeiro-Filho OG, Arriaga Camargo LO, Valdenegro-Toro MA, et al. Optimization of convolutional neural network hyperparameters for automatic classification of adult mosquitoes. PLoS One. 2020;15(8):e0237151.\u003c/li\u003e\n\u003cli\u003eKittichai V, Kaewthamasorn M, Samung Y, Jomtarak R, Naing KM, Tongloy T, et al. Automatic identification of medically important mosquitoes using embedded learning approach-based image-retrieval system. Sci Rep. 2023;13(1):13072.\u003c/li\u003e\n\u003cli\u003eZhao OS, Kolluri N, Anand A, Chu N, Bhavaraju R, Ojha A, et al. Convolutional neural networks to automate the screening of malaria in low-resource countries. PeerJ. 2020;8:e8965.\u003c/li\u003e\n\u003cli\u003eValan M, Makonyi K, Maki A, Vondr\u0026aacute;ček D, Ronquist F. Automated Taxonomic Identification of Insects with Expert-Level Accuracy Using Effective Feature Transfer from Convolutional Networks. Syst Biol. 2019;68(5):876-895.\u003c/li\u003e\n\u003cli\u003eKrizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM. 2017;60(6):84\u0026ndash;90.\u003c/li\u003e\n\u003cli\u003ede Miranda VL, de Souza EP, Bambil D, Khalighifar A, Peterson AT, de Oliveira Nascimento FA, et al. Cellphone picture-based, genus-level automated identification of Chagas disease vectors: Effects of picture orientation on the performance of five machine-learning algorithms. Ecol Inform. 2024;79:101660.\u003c/li\u003e\n\u003cli\u003eHarrell FE. Hmisc: a package of miscellaneous R functions. 2014. Available from: http://biostat.mc.vanderbilt.edu/Hmisc.\u003c/li\u003e\n\u003cli\u003eRStudio: Integrated Development for R. RStudio, Version 4.4.0. Vienna: R Foundation for Statistical Computing. 2024. Available from: https://posit.co/products/open-source/rstudio/.\u003c/li\u003e\n\u003cli\u003eR Development Core Team. R: a language and environment for statistical computing. Version 4.4.0. Vienna: R Foundation for Statistical Computing. 2024. Available from: https://www.R-project.org.\u003c/li\u003e\n\u003cli\u003eLorenz C, Ferraudo AS, Suesdek L. Artificial Neural Network applied as a methodology of mosquito species identification. Acta Trop. 2015;152:165\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eSauer FG, Werny M, Nolte K, Villaca\u0026ntilde;as de Castro C, Becker N, Kiel E, et al. A convolutional neural network to identify mosquito species (Diptera: Culicidae) of the genus Aedes by wing images. Sci Rep. 2024;14:14147.\u003c/li\u003e\n\u003cli\u003eOkayasu K, Yoshida K, Fuchida M, Nakamura A. Vision-based classification of mosquito species: Comparison of conventional and deep learning methods. Appl Sci. 2019;9(18):3818.\u003c/li\u003e\n\u003cli\u003eGoodwin A, Padmanabhan S, Hira S, Glancey M, Slinowsky M, Immidisetti R, et al. Mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection. Sci Rep. 2021;11:14394.\u003c/li\u003e\n\u003cli\u003ePora W, Kasamsumran N, Tharawatcharasart K, Ampol R, Siriyasatien P, Jariyapan N. Enhancement of VGG16 model with multi-view and spatial dropout for classification of mosquito vectors. PLoS One. 2023;18(7):e0253793.\u003c/li\u003e\n\u003cli\u003eConsoli RAGB, Oliveira RL de. Principais mosquitos de import\u0026acirc;ncia sanit\u0026aacute;ria no Brasil. 1st ed. Rio de Janeiro: Editora Fiocruz; 1994.\u003c/li\u003e\n\u003cli\u003eGurgel-Gon\u0026ccedil;alves R, Komp E, Campbell LP, Khalighifar A, Mellenbruch J, Mendon\u0026ccedil;a VJ, et al. Automated identification of insect vectors of Chagas disease in Brazil and Mexico: The Virtual Vector Lab. PeerJ. 2017;5:e3040.\u003c/li\u003e\n\u003cli\u003eKhalighifar A, Komp E, Ramsey JM, Gurgel-Gon\u0026ccedil;alves R, Peterson AT. Deep Learning Algorithms Improve Automated Identification of Chagas Disease Vectors. J Med Entomol. 2019;56(2):461-468.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"parasites-and-vectors","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"parv","sideBox":"Learn more about [Parasites \u0026 Vectors](http://parasitesandvectors.biomedcentral.com/)","snPcode":"13071","submissionUrl":"https://submission.nature.com/new-submission/13071/3","title":"Parasites \u0026 Vectors","twitterHandle":"@bugbittentweets","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Deep Learning, Machine learning, Culicidae","lastPublishedDoi":"10.21203/rs.3.rs-4473317/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4473317/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eIdentifying mosquito vectors is crucial for controlling diseases. Automated identification studies using Convolutional Neural Network (CNN) have been conducted for some urban mosquito vectors but not yet for sylvatic mosquito vectors that transmit the yellow fever and other arboviruses. Here, we evaluated the ability of the AlexNet CNN to identify four mosquito species: \u003cem\u003eAedes serratus\u003c/em\u003e, \u003cem\u003eAedes scapularis\u003c/em\u003e, \u003cem\u003eHaemagogus leucocelaenus\u003c/em\u003e and \u003cem\u003eSabethes albiprivus\u003c/em\u003e and whether there is variation in AlexNet's ability to classify mosquitoes based on pictures of four different body regions.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe specimens were photographed using a cell phone connected to a stereoscope. Photographs were taken of the full-body, pronotum and lateral view of the thorax, which were pre-processed to train the AlexNet algorithm. The evaluation was based on the confusion matrix, the accuracy (10 pseudoreplicates) and the confidence interval for each experiment.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur study found that the AlexNet can accurately identify mosquito pictures of the genus \u003cem\u003eAedes\u003c/em\u003e, \u003cem\u003eSabethes\u003c/em\u003e and \u003cem\u003eHaemagogus\u003c/em\u003e with over 90% accuracy. Furthermore, the algorithm performance did not change according to the body regions submitted. It is worth noting that the state of preservation of the mosquitoes, which were often damaged, may have affected the network's ability to differentiate between these species and thus accuracy rates could have been even higher.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur results support the idea of applying CNNs for AI-driven identification of mosquito vectors of tropical diseases. This approach can potentially improve the surveillance of yellow fever vectors by health services and the population as well.\u003c/p\u003e","manuscriptTitle":"AI-driven convolutional neural networks for accurate identification of yellow fever vectors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-06 15:06:58","doi":"10.21203/rs.3.rs-4473317/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-21T14:55:02+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-19T10:43:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-01T07:34:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"325335636729173657139047019120957516445","date":"2024-05-29T06:53:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"7276084386914046560731744295714661506","date":"2024-05-28T15:01:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"208402557129808754489794470619532004626","date":"2024-05-27T09:44:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-26T22:56:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-25T11:51:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-25T11:46:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Parasites \u0026 Vectors","date":"2024-05-24T15:18:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"parasites-and-vectors","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"parv","sideBox":"Learn more about [Parasites \u0026 Vectors](http://parasitesandvectors.biomedcentral.com/)","snPcode":"13071","submissionUrl":"https://submission.nature.com/new-submission/13071/3","title":"Parasites \u0026 Vectors","twitterHandle":"@bugbittentweets","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d3aa2c9c-ebf0-42cd-a013-0c17d072fce1","owner":[],"postedDate":"June 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-05T16:02:04+00:00","versionOfRecord":{"articleIdentity":"rs-4473317","link":"https://doi.org/10.1186/s13071-024-06406-2","journal":{"identity":"parasites-and-vectors","isVorOnly":false,"title":"Parasites \u0026 Vectors"},"publishedOn":"2024-08-02 15:57:24","publishedOnDateReadable":"August 2nd, 2024"},"versionCreatedAt":"2024-06-06 15:06:58","video":"","vorDoi":"10.1186/s13071-024-06406-2","vorDoiUrl":"https://doi.org/10.1186/s13071-024-06406-2","workflowStages":[]},"version":"v1","identity":"rs-4473317","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4473317","identity":"rs-4473317","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}