A Comparative Study of Different CNN Architectures for Real-World Image Classification in Bangladesh

A Comparative Study of Different CNN Architectures for Real-World Image Classification in Bangladesh | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report A Comparative Study of Different CNN Architectures for Real-World Image Classification in Bangladesh Md. Sadmin Tahmid Khan, Ahad Bin Islam Shoeb, Arif Billah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8562599/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Convolutional Neural Networks (CNNs) are widely used for image classification, yet their performance strongly depends on dataset complexity and deployment constraints. This study presents a comparative evaluation of custom-designed CNN architectures and popular pre-trained models on five real-world image datasets from Bangladesh, spanning agricultural and infrastructural applications. The tasks include mango variety classification (15 classes), paddy disease clas-sification (35 classes), and three binary classification problems: road damage, footpath encroachment, and auto-rickshaw detection. In addition to task-specific CNNs, VGG16 and ResNet50 are evaluated using fixed feature extraction and transfer learning strategies. The results show that transfer learning, particularly with ResNet50, achieves the highest accuracy on complex multi-class datasets, while custom CNNs deliver competitive performance on binary tasks with sub-stantially lower computational cost. These findings emphasize the trade-off between accuracy and efficiency and highlight the importance of selecting model architectures based on dataset characteristics and deployment requirements. Artificial Intelligence and Machine Learning Convolutional Neural Network Image Classification Vgg16 Resnet50 Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8562599","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":572182484,"identity":"88fa836b-cac8-4b20-bbe9-9aed7e0ba3c4","order_by":0,"name":"Md. 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