Gas-Net: A deep neural network for gastric tumor semantic segmentation

Currently, the gastric cancer is the source of the high mortality rate where it is diagnoses from the stomach and esophagus tests. To this end, the whole of studies in the analysis of cancer are built on AI (artificial intelligence) to develop the analysis accuracy and decrease the danger of death. Mostly, deep learning methods in images processing has made remarkable advancement. In this paper, we present a method for detection, recognition and segmentation of gastric cancer in endoscopic images. To this end, we propose a deep learning method named GAS-Net to detect and recognize gastric cancer from endoscopic images. Our method comprises at the beginning a preprocessing step for images to make all images in the same standard. After that, the GAS-Net method is based an entire architecture to form the network. A union between two loss functions is applied in order to adjust the pixel distribution of normal/abnormal areas. GAS-Net achieved excellent results in recognizing lesions on two datasets annotated by a team of expert from several disciplines (Dataset1, is a dataset of stomach cancer images of anonymous patients that was approved from a private medical-hospital clinic, Dataset2, is a publicly available and open dataset named HyperKvasir ‎[1]). The final results were hopeful and proved the efficiency of the proposal. Moreover, the accuracy of classification in the test phase was 94.06%. This proposal offers a specific mode to detect, recognize and classify gastric tumors.