PCOS-Vision A Hybrid Deep Learning Model for Polycystic Ovary Syndrome Detection using MobileNetV2 and Clinical Data

DOI

10.2991/978-94-6463-718-2_74How to use a DOI?

Keywords

PCOS; deep learning; MobileNetV2; hybrid models; clinical data; ultrasound imaging; early diagnosis; artificial intelligence; reproductive health; healthcare innovation

Abstract

Due to different presentations in females, polycystic ovary syndrome (PCOS) is the most common endocrine disorder and is particularly likely to be underdiagnosed and misdiagnosed. Recent machine learning structures such as MobileNetV2 offer promising opportunities for accurate, efficient and cost-effective PCOS diagnosis. The hybrid models generate heavier and strikingly strengthen in early diagnosis also minimizing human error. Multi-modal data allows for improved accuracy while the resource-efficient nature of models such as MobileNetV2 allows it to be deployed in mobile and resource-constrained settings. AI-powered solutions also streamline the diagnostic experience, yielding repeatable, standardized outputs that align with clinical pathways and workflows. With the aid of recent advances in deep learning, the findings from these studies have the power to change the landscape of reproductive health, by demonstrating earlier and more accurate PCOS detection as a feasible option in a wider set of patient and healthcare settings.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - R. Vijayakumar
AU  - S. Prabhakaran
AU  - G. Venkatesh
AU  - D. Sathiya
AU  - M. Azhagesan
AU  - P. Palanisamy
PY  - 2025
DA  - 2025/05/23
TI  - PCOS-Vision A Hybrid Deep Learning Model for Polycystic Ovary Syndrome Detection using MobileNetV2 and Clinical Data
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 865
EP  - 877
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_74
DO  - 10.2991/978-94-6463-718-2_74
ID  - Vijayakumar2025
ER  -