Automated Detection of Polycystic Ovary Syndrome Using Convolutional Neural Networks on Ultrasound Images
DOI:
https://doi.org/10.56294/saludcyt20252272Keywords:
Polycystic Ovary Syndrome, Convolution Neural Network, Women Health, AI driven Solution, Diagnostics, HealthcareAbstract
Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine disorder affecting millions of women worldwide, yet it remains frequently underdiagnosed due to symptom variability and limited diagnostic resources. This paper presents a Convolutional Neural Network (CNN)-based system for automated PCOS detection from ultrasound images. The model leverages deep learning for accurate feature extraction and classification, aiming to support clinicians and improve diagnostic accessibility. Experimental results demonstrate high accuracy, underscoring the potential of AI-driven solutions in advancing women’s healthcare. Beyond accuracy, the system offers scalability, reduced diagnostic time, and potential integration into telemedicine platforms, highlighting its role in bridging healthcare gaps and enabling earlier intervention.
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Copyright (c) 2025 Varshitha D N, Gowrishankar B S, Sailaja Mulakaluri, Chaitra Nayak J, Savita Choudhary, Varshiya T V, Sanjana B N (Author)
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The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
