A DEEP LEARNING APPROACH TO PCOS DIAGNOSIS: TWO-STREAM CNN WITH TRANSFORMER ATTENTION MECHANISM

Abstract

Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine disorder affecting a significant portion of women worldwide, often underdiagnosed due to the complexity of its symptoms and limitations in existing diagnostic tools. With advancements in deep learning and medical imaging, automated classification systems offer the potential to revolutionize PCOS detection through precision and scalability. This study proposes a novel Two-Stream Convolutional Neural Network (CNN) architecture enhanced with Transformer-based attention mechanisms for classifying PCOS from ultrasound images. Leveraging dataset of 11,784 images, our framework splits each ultrasound image into upper and lower halves to capture anatomical variance and apply convolutional encoding separately. A Multi-Head Attention layer then integrates spatial dependencies between the two streams, enhancing feature discrimination and improving model interpretability. Experimental evaluations show that the proposed model achieves a classification accuracy of 98.96%, an F1-score of 0.99, and minimal loss on the test dataset. These results highlight the model’s robustness and potential applicability in real-world clinical settings for the early detection of PCOS.