Abstract: Aiming at the problems such as few applications, low detection accuracy and slow detection speed of the mainstream object detection algorithms in the detection of benign and malignant masses in images of mammogram, a mass detection model of breast mammogram based on improved YOLOv4 model is proposed. This method can simultaneously detect and classify masses efficiently in a framework. Firstly, the detection model introduces a multi-channel JAnet residual structure to improve the backbone network of the model. Secondly, the depthwise separable convolution is introduced to replace the standard convolution in the original YOLOv4 model. Finally, a larger value averaging method is proposed in the post-processing stage. In the experiments, the DDSM (Digital Database for Screening Mammography) data set is used as the training set to train the detection model, and the INbreast data set is used as the independent test set. The experimental results show that, compared with the original YOLOv4 model, the proposal increases the Recall, mAP, FPS, and AUC by 7.3%, 6.45%, 5.9 fps and 13.02% respectively. The overall effect of the model is better than that of the current mainstream object detection model, showing good robustness and effectiveness. The model can play a role in computer-aided diagnosis in the clinical diagnosis of breast cancer by doctors.

Key words: mass detection, breast mammogram, JAnet, depthwise separable convolution, YOLOv4