Enhancing Breast Cancer Diagnosis with YOLOv5: A Computer-Aided Approach

Breast cancer is still a major worldwide health problem that requires prompt and precise diagnosis techniques in order to effectively treat and control the disease. In this work, we introduce a new method for computer-assisted breast cancer diagnosis based on the YOLOv5 algorithm, which is well-known for its effectiveness and precision in object identification tasks. By using a comprehensive dataset comprising diverse mammography images, the proposed methodology involves training the YOLOv5 model to detect and classify suspicious regions indicative of breast cancer lesions. The trained model demonstrates exceptional performance, achieving high detection sensitivity and specificity. Furthermore, we demonstrate the practical usefulness of our method in supporting radiologists by offering quick and accurate evaluations of breast anomalies, which promotes early intervention and enhances patient outcomes. Our findings demonstrate the potential of deep learning algorithms, such YOLOv5, to improve breast cancer detection skills, providing a useful instrument for improving oncology healthcare practices.