YOLO-NAS Based Deep Learning Approach for Breast Lesion Identification

Early and accurate breast cancer detection is crucial for effective intervention and improved patient outcomes. Conventional methods often encounter challenges in achieving the required accuracy. To address this, we advocate for the utilization of the YOLO-NAS architecture, renowned for its proficiency in object detection. Implementation of YOLO-NAS explores the efficacy of breast cancer identification, utilizing a diverse dataset for comprehensive evaluation. The employed model demonstrates exceptional accuracy, boasting a mean Average Precision (mAP) value of 87.7 and an impressive recall of 98%. Despite its computational demands, the model's promising performance suggests its potential as a valuable tool in medical diagnostics. Ongoing efforts will focus on optimizing computational efficiency and ensuring the seamless integration of the model into clinical practices for widespread and effective usage.