Medical image recognition and segmentation of pathological slices of gastric cancer based on Deeplab v3+ neural network. (August 2021) - Wang, Jing; Liu, Xiuping
Highlights: An automatic gastric cancer segmentation model based on Deeplab v3+ is proposed. Deeplab v3+ with multi-scale input can improve performance. Deeplab v3+ improves accuracy by more than 12% compared to SegNet and ICNet. Deeplab v3+ only occupies 2.42GB and training time only takes 12.42 h. Deeplab v3+ model predicts more cancerous areas than the other models. 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.