Automated identification of gastric cancer in endoscopic images by a deep learning model

Gastric cancer is a deadly disease which should be treated in time, in order to increase the life span of the patient. Computer aided diagnosis will help the doctors to identify the gastric cancer easily. In this paper, a CAD based approach is projected to discriminate and categorize gastric cancers from various other intestinal disorders. The approach provided the Xception network, with individual convolutions. The projected technique applied three procedures: Google’s Auto Augment for augmentation purpose, BCGDU-Net for segmentation and Xception network for lesion classification. The augmentation and segmentation facilitated theclassifying technique to be enhanced because this methodology prohibited overfitting. The segmented region is classified as cancerous or non-cancerous based on the features extracted in the Xception network training phase. This method is analyzed with the different combinations of augmentation, segmentation with and without ROC. It is found that the area under ROC curve for augmentation and segmentation is higher than the other two cases. Moreover, this technique provides a segmentation accuracy of 98% when compared with existing methods like fuzzy C means, global thresholding, BCD-Net, U Net. The classification accuracy of 98.9% is obtained, which is higher than the existing techniques like Res Net, VGG net, Mobile Net.