PCOSCnnLstm: A Hybrid Deep Learning Approach for Polycystic Ovary Syndrome Detection

PCOSCnnLstm: A Hybrid Deep Learning Approach for Polycystic Ovary Syndrome Detection | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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. 11 August 2025 V1 Latest version Share on PCOSCnnLstm: A Hybrid Deep Learning Approach for Polycystic Ovary Syndrome Detection Authors : Sonia Akter and Fadel Rabby Authors Info & Affiliations https://doi.org/10.22541/au.175493179.90737331/v1 204 views 182 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Polycystic Ovary Syndrome (PCOS) is often difficult to diagnose because of symptom overlap and wide variations in clinical presentation. In this work, we present an advanced deep learning model that merges Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks to boost diagnostic performance using standard clinical datasets. The framework is designed to recognize spatial data patterns and monitor changes in symptoms over time through a late-fusion strategy applied to extracted features. A Pearson correlation–driven selection process identified 23 key biomarkers from records of 541 patients, while the Synthetic Minority Oversampling Technique (SMOTE) was applied to address class imbalance within a 5-fold cross-validation scheme. The proposed CNN-LSTM hybrid achieved superior outcomes compared with standalone CNN or LSTM models, recording a top accuracy of 96.58%, a precision of 96.86%, and an AUC score of 95.76%. Relative to leading contemporary methods, it reduced false negatives by more than 22%, setting a higher standard for timely and accurate PCOS diagnosis. Supplementary Material File (pcoscnnlstm a hybrid deep learning approach for polycystic ovary.pdf) Download 1.68 MB Information & Authors Information Version history V1 Version 1 11 August 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords cnn lstm pearson correlation coefficience polycystic ovary syndrome Authors Affiliations Sonia Akter Bangladesh Army International University of Science and Technology View all articles by this author Fadel Rabby Ahsanullah University of Science and Technology View all articles by this author Metrics & Citations Metrics Article Usage 204 views 182 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Sonia Akter, Fadel Rabby. PCOSCnnLstm: A Hybrid Deep Learning Approach for Polycystic Ovary Syndrome Detection. Authorea . 11 August 2025. DOI: https://doi.org/10.22541/au.175493179.90737331/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. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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