Authors - Kamini Solanki, Nilay Vaidya, Jaimin Undavia, Jay Panchal Abstract - Polycystic ovary disease (PCOD) is a condition in which the ovaries of women of childbearing age produce too many immature or partially mature eggs. As time passes, these eggs develop into cysts within the ovaries. These cysts can lead to enlargement of the ovaries and an elevated production of male hormones (androgens). Consequently, this hormonal imbalance can result in a range of issues like fertility challenges, irregular menstrual cycles, unanticipated weight gain, and various other health complications. The associated symptoms often exert a long-term impact on both the physical and mental well- being of affected women. Statistics indicate that approximately 34% of individuals facing PCOD also grapple with depressive symptoms, while almost 45% experience anxiety. The primary object of this proposed framework is to detect and classify PCOD disease from standard X-ray pictures with assistance of volume datasets using deep learning model. Polycystic Ovary Disease (PCOD) significantly affects women's reproductive health, leading to various long-term complications. This work introduces a novel framework for automated PCOD detection using integrating Convolutional Neural Networks (CNN) with deep learning, applied to ultrasound imaging. Unlike traditional diagnostic methods, which rely on manual interpretation and are prone to subjectivity, the proposed system leverages the powerful feature extraction capabilities of CNNs to classify infected and non-infected ovaries with 100% accuracy. This high level of precision outperforms existing models and can be seamlessly integrated into clinical workflows for real-time diagnosis during sonography, facilitating early detection and improved fertility management. By focusing on a deep learning approach, this work provides a scalable, reliable, and automated solution for PCOD diagnosis, marking a significant advancement in the use of medical imaging with artificial intelligence.
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