Deep Learning--Assisted Vaginal Cytology for Estrus Classification in Dogs and Cats

Deep Learning--Assisted Vaginal Cytology for Estrus Classification in Dogs and Cats | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 21 May 2025 V1 Latest version Share on Deep Learning--Assisted Vaginal Cytology for Estrus Classification in Dogs and Cats Authors : Muruvvet Kalkan MSc 0000-0001-8056-1905 , Mert Turanli MSc , Muhammed Uz , and Cahit Kalkan Authors Info & Affiliations https://doi.org/10.22541/au.174781114.48367566/v1 489 views 200 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Vaginal cytology is a diagnostic tool for evaluating estrous cycle stages and reproductive health in female dogs and cats. It involves microscopic examination of vaginal epithelial cells, but subjective interpretation can lead to inconsistencies. This study explores artificial intelligence (AI), specifically deep learning, to enhance accuracy. A total of 1,096 vaginal smear samples were collected, stained, digitized, and analyzed using AI. Several pre-trained convolutional neural networks (CNNs), including MobileNetV2, ResNet152V2, EfficientNetV2L, Xception, VGG-16, InceptionV3, NasNetLarge, InceptionResNetV2, DenseNet201, and ConvNeXtSmall, were evaluated. The Xception model achieved the highest accuracy at 97.65%. These findings demonstrate AI’s potential to reduce subjectivity, improve diagnostic consistency, and advance reproductive health assessments in veterinary medicine. Supplementary Material File (manuscript.pdf) Download 1.52 MB Information & Authors Information Version history V1 Version 1 21 May 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords deep learning artificial intelligence classification estrus cycle vaginal cytology Authors Affiliations Muruvvet Kalkan MSc 0000-0001-8056-1905 Ankara Universitesi View all articles by this author Mert Turanli MSc Firat Universitesi View all articles by this author Muhammed Uz Firat Universitesi View all articles by this author Cahit Kalkan Firat Universitesi View all articles by this author Metrics & Citations Metrics Article Usage 489 views 200 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Muruvvet Kalkan MSc, Mert Turanli MSc, Muhammed Uz, et al. Deep Learning--Assisted Vaginal Cytology for Estrus Classification in Dogs and Cats. Authorea . 21 May 2025. DOI: https://doi.org/10.22541/au.174781114.48367566/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. 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