YOLO algorithm improves diagnostic performance of mammography: More than eyes

Abstract

Background. Breast cancer (BC) is now the most common malignancy in women. Early detection and precise diagnosis are essential for improving survival. Objectives. To develop an integrated computer-aided diagnosis (CAD) system that automatically detects, segments and classifies lesions in mammographic images, thereby aiding BC diagnosis. Materials and methods. We adopted YOLOv5 as the object-detection backbone and used the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM). Data augmentation (random rotations, crops and flips) increased the dataset to 5,801 images, which were randomly split into training, validation and test sets (7 : 2 : 1). Lesion-classification performance was evaluated with the area under the receiver operating characteristic (ROC) curve (AUC), precision, recall, and mean average precision at a 0.5 confidence threshold (mAP@0.5). Results. The CAD system yielded an mAP@0.5 of 0.417 and an F1-score of 0.46 for lesion detection, achieved an AUC of 0.90 for distinguishing benign from malignant lesions, and processed images at 65 fps. Conclusions. The integrated CAD system combines rapid detection and classification with high accuracy, underscoring its strong clinical value.