基于改进YOLOv4模型的乳腺钼靶图像肿块检测

Mass detection of breast mammogram based on improved YOLOv4 model

针对目前主流目标检测算法在乳腺钼靶图像的良恶性肿块目标检测中存在应用较少、检测准确率低和检测速度慢等问题,提出一种基于改进YOLOv4的乳腺钼靶图像肿块检测模型。该方法可以在一个框架中同时高效进行肿块的检测和分类。首先,引入了分流聚合双通道(JAnet)残差结构对模型的骨干网络进行改进;其次,引入深度可分离卷积来替换原YOLOv4模型中的标准卷积;最后,在后处理阶段提出了较大值求平均方法。以DDSM数据集作为训练集训练检测模型,并以INbreast数据集作为独立测试集。实验结果表明,提出的基于改进YOLOv4的乳腺钼靶图像肿块检测模型的Recall值、mAP值、FPS和AUC值相比原YOLOv4算法的分别提高了7.3%,6.45%,5.9 fps和13.02%。模型整体效果优于目前主流的目标检测模型的,体现出了良好的鲁棒性和有效性,可以在医师对乳腺癌临床诊断过程中发挥计算机辅助诊断作用。

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.