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.
Material 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.
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