Detecting and classifying breast masses via YOLO-based deep learning

Abstract Breast cancer has a high incidence and mortality rate in the female population. Mammography is the most reliable method for early and accurate diagnosis of breast cancer. Automated detection and classification of breast masses on mammograms is a challenging task and is essential to assist radiologists in accurately diagnosing breast masses. The aim of this study is to develop a Computer-Aided Diagnosis (CAD) system based on You Look Only Once (YOLO) for identifying breast masses and classifying them as benign or malignant. We propose a YOLOv5-CAD framework that uses a transfer learning approach. Two datasets, CBIS-DDSM and VinDr-Mammo, are utilized for training from scratch. The model weights and parameters are subsequently transferred and fine-tuned onto the smaller INBreast dataset. Furthermore, an analysis is conducted to assess the impact of various data augmentation techniques during the training phase on enhancing model performance. The proposed framework demonstrates encouraging fivefold cross-validation evaluation results. To conclude, transfer learning from CBIS-DDSM achieves 0.843 mAP, precision of 0.855, recall of 0.774, while transfer learning from VinDr- Mammo reaches 0.84 mAP, precision of 0.829, recall of 0.787. Furthermore, the performance of the two fine-tuned models was tested on both the MIAS dataset and the private dataset from Başkent University Ankara Hospital. Such promising performance could be useful for the CAD frameworks being developed to support radiologists as a second opinion reader for the detection and classification of breast masses.