Gastric cancer pathological image diagnosis system based on ResNet and UNet
Considering that manual identification and diagnosis of gastric cancer pathological images may cause missed detection and in order to make diagnosis more accurate,a pathological image diagnosis system based on ResNet and UNet is proposed,aiming to classify,segment and output the diagnosis results of pathological images.The ResNet model is used to classify gastric cancer pathological images with and without cancer.The UNet model is improved,and the improved model adds a convolutional block attention module before each down-sampling and up-sampling to enhance the model's attention to cancerous areas.The residual module is used to replace the two convolutions in the encoding part to improve feature utilization;and the Inception module is used to replace the two convolutions in the up-sampling of the decoding part,thereby expanding its width to obtain features of different scales.The classification and segmentation results are comprehensively considered to obtain the final diagnostic results of gastric cancer pathological images.Experimental results show that this system can effectively diagnose the presence of cancer in gastric cancer pathological images.
pathological imagesimage classificationUNetimage segmentationdiagnosis of gastric cancer
NETL
NSTL
万方数据
