Medical image recognition and segmentation of pathological slices of gastric cancer based on Deeplab v3+ neural network

In order to improve the efficiency of gastric cancer pathological slice image recognition and segmentation of cancerous regions, this paper proposes an automatic gastric cancer segmentation model based on Deeplab v3+ neural network. Based on 1240 gastric cancer pathological slice images, this paper proposes a multi-scale input Deeplab v3+ network, _and compares it with SegNet, ICNet in sensitivity, specificity, accuracy, and Dice coefficient. The sensitivity of Deeplab v3+ is 91.45%, the specificity is 92.31%, the accuracy is 95.76%, and the Dice coefficient reaches 91.66%, which is more than 12% higher than the SegNet and Faster-RCNN models, and the parameter scale of the model is also greatly reduced. Our automatic gastric cancer segmentation model based on Deeplab v3+ neural network has achieved better results in improving segmentation accuracy and saving computing resources. Deeplab v3+ is worthy of further promotion in the medical image analysis and diagnosis of gastric cancer.