Detection and Classification of Lesions in Mammograms using One-Stage Models.

Background: Breast cancer, the most common cancer among women, necessitates early detection. Despite advances in Computer-Aided Diagnosis (CAD), lesion detection in mammograms remains challenging. Artificial Intelligence (AI) in radiology offers significant potential to enhance diagnostic accuracy in medical imaging. Objective: This study compares object detection methods to identify the most effective model for smart diagnostic systems. This comprehensive study is the first to apply the advanced You Only Look Once version 12 (YOLO-v12) architecture for the automated detection and localization of lesions in mammographic images and to identify their malignancy or benignity status with high precision. Material and Methods: This comparative experimental study, utilizing retrospective data, also evaluated two state-of-the-art models, the Detection Transformer (DETR) and RetinaNet, for their performance. The models were trained and tested on the publicly available Categorized Digital Database for Low-Energy and Subtracted Contrast-Enhanced Spectral Mammography (CDD-CESM), which contains 1,982 mammograms with 3,720 annotated lesions of various types and sizes. Results: YOLO-v12 demonstrated excellent diagnostic accuracy (mean Average Precision at an IOU threshold of 0.5 (mAP50)=0.98; Intersection Over Union (IOU)=0.95), significantly outperforming contemporary models and older YOLO versions. Conclusion: The promising and robust results clearly underscore the remarkable potential of artificial intelligence technologies in effectively assisting radiologists with the early detection and diagnosis of breast cancer. These findings advocate for the implementation of YOLO-v12 in clinical mammography screening applications and suggest that future research should prioritize real-time diagnostic systems to further enhance breast cancer detection capabilities.