Deep Learning-Based Automated Detection of Polycystic Ovary Syndrome (PCOS) using Supervised Machine Learning

Polycystic Ovary Syndrome (PCOS) is a common endocrine disorder affecting $\mathbf{8 - 1 3 \%}$ of women of reproductive age, typically diagnosed through clinical, biochemical, and ultrasound imaging methods. Traditional diagnostic approaches are time-consuming, rely on expert interpretation, and are prone to variability, highlighting the need for more accurate and efficient solutions. This study investigates the use of deep learning for automated PCOS detection using ultrasound imaging, employing convolutional neural networks (CNNs) for feature extraction and classification. The proposed system achieved high accuracy, sensitivity, and specificity, outperforming conventional methods. By automating the detection process, this approach reduces reliance on subjective interpretations, ensuring consistent and reliable results. This research demonstrates the transformative potential of machine learning in medical diagnostics, particularly for early PCOS detection and management, offering scalable and accessible healthcare solutions.