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

Abstract

Objective: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

Keywords: Convolutional neural network; Deeplab v3+; Gastric cancer pathological slice image; Image segmentation; Multi-scale input.