Detection of Poly-Cystic Ovarian Syndrome (PCOS) Using Image Processing Techniques

Poly-Cystic Ovary Syndrome (PCOS), a common hormonal disorder in women of reproductive age, requires timely and accurate diagnosis for effective management. The paper offers a deep learning-based method for detecting polycystic ovarian syndrome (PCOS) by analyzing ultrasound pictures using convolutional neural networks (CNNs). Data collection, preprocessing, feature extraction, model selection, training, assessment, and clinical validation are all included in our methodology. The CNN model was trained using a labeled ultrasound image dataset that was purchased from Rajalakshmi Health City. For classification, important morphological and textural characteristics were taken out, such as the number of cysts, their location, volume, ovary diameter, and S/A ratio. The model, which was optimized using cutting-edge methods, showed excellent accuracy, precision, recall, and F1 score. Its dependability, commitment to moral principles, and conformity with patient privacy regulations were validated clinically with medical experts. Our results demonstrate how AI-driven diagnostics can advance early intervention techniques, improve PCOS detection, and improve medical imaging applications.