YOLO algorithm improves diagnostic performance of mammography: More than eyes.
Authors
Affiliations (5)
Affiliations (5)
- Department of Radiology, Jining No. 1 People's Hospital, China.
- Department of Anesthesiology, Affiliated Hospital of Jining Medical University, China.
- Department of Oncology, Jining No. 1 People's Hospital, China.
- Translational Pharmaceutical Laboratory, Jining No. 1 People's Hospital, China.
- Clinical Research Center, Jining No. 1 People's Hospital, China.
Abstract
Breast cancer (BC) is now the most common malignancy in women. Early detection and precise diagnosis are essential for improving survival. To develop an integrated computer-aided diagnosis (CAD) system that automatically detects, segments and classifies lesions in mammographic images, thereby aiding BC diagnosis. 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 ([email protected]). The CAD system yielded an [email protected] 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. The integrated CAD system combines rapid detection and classification with high accuracy, underscoring its strong clinical value.