Enhancing Polycystic Ovary Syndrome (PCOS) Diagnosis Using Machine Learning Techniques

Polycystic Ovary Syndrome (PCOS) is a prevalent medical condition affecting women and is associated with hormonal imbalances, infertility, and irregular menstruation. Accurate and early diagnosis of PCOS is critical for effective treatment and management. This study explores the application of a Siamese Neural Network in diagnosing PCOS using ovarian ultrasound images. By comparing extracted features from healthy and abnormal ovarian images, the model provides a robust method to identify PCOS-specific abnormalities, such as cystic formations, thickened ovarian tissues, and disrupted vascular patterns. Advanced image enhancement techniques are employed to preprocess the data, while the Siamese Network analyzes structural and histological features to determine the presence of PCOS. The results demonstrate the model's ability to detect significant variations between healthy and abnormal ovaries, including hormonal patterns, blood vessel distributions, and tissue abnormalities and the proposed model achieves reliable classification accuracy, offering a potential tool for aiding clinicians in early PCOS detection and this research highlights the transformative role of machine learning, particularly Siamese Networks, in medical imaging and diagnosis, paving the way for transparent and efficient healthcare solutions. Further advancements are recommended to incorporate additional data sources and predictive treatment tools for enhanced patient outcomes.