Deep Learning Based Polycystic Ovary Syndrome (PCOS) Identification

Polycystic ovary syndrome (PCOS) is a common endocrine disorder affecting reproductive-aged women, characterized by hormonal imbalance and ovarian cysts. Timely and accurate diagnosis of PCOS is crucial for effective management and prevention of associated complications. In this paper, we propose an automated diagnostic system for PCOS using deep learning and convolutional neural networks (CNNs). The dataset comprises ultrasound images of ovaries categorized into infected (PCOS) and not infected (non-PCOS) classes. Two CNN architectures, a deep CNN model and a CNN model with a last layer replaced by a support vector machine (SVM), are implemented and evaluated. Evaluation metrics include accuracy, precision, recall, and F1-score, demonstrating promising performance in accurately distinguishing between PCOS and non-PCOS cases. This automated diagnostic system offers a reliable and efficient approach for PCOS diagnosis, potentially reducing dependence on manual interpretation of ultrasound scans and improving healthcare outcomes for affected individuals.