PCOS DETECTION USING MACHINE LEARNING

Abstract Polycystic Ovary Syndrome (PCOS) is one of the most common causes of female infertility, affecting a large number of women of reproductive age and often persisting beyond the childbearing years. This hormonal disorder can result in several long-term health complications, making early detection and timely intervention crucial. In this study, we propose a deep learning- based approach using Convolutional Neural Networks (CNN) for the early diagnosis of PCOS by analyzing medical imaging data, including sonography scans and lumbar MRI images. The CNN model effectively identifies patterns and abnormalities associated with PCOS, enhancing diagnostic accuracy and improving overall clinical outcomes. The model leverages advanced image processing techniques and deep learning algorithms to classify PCOS cases with high precision. This approach facilitates the development of personalized treatment plans, enabling healthcare professionals to provide targeted therapy and better symptom management. By integrating cutting-edge technology with medical expertise, this method empowers women to take proactive control of their reproductive health and overall well-being. The proposed model demonstrates significant improvements in diagnostic performance and offers a reliable, automated solution for PCOS detection in clinical settings. Index Terms — PCOS, CNN, Medical Imaging, Early Detection, Personalized Treatment, Deep Learning, Reproductive Health,