A Structure for Forecasting Stomach Cancer Using Deep Learning and Advanced Tongue Characteristics

The global health problem of gastric cancer has significantly impacted people’s daily lives. Early detection and rapid treatment of stomach cancer patients considerably contribute in protecting human health. Routine gastric cancer tests, however, are time-consuming and run the risk of consequences. The performance of the current tongue segmentation algorithms in a typical context is good. Tongue segmentation is quickly completed in an open environment using a revolutionary deep neural network tongue segmentation technique that is appropriate for mobile devices. Given that the images taken by this device also include non-tongue areas, a special deep neural network was used to segment the tongue in order to reduce interference with feature extraction. Intelligent tongue diagnosis requires accurate tongue picture segmentation. In this manner, linkages between the nine recovered tongue traits and the disease as well as statistical and deep learning approaches were used to construct a forecast structure to predict gastric cancer. The experimental results demonstrated the suggested framework’s 93.6% accuracy rate for threat detection. A method for investigating the connections between cancer of the stomach and tongue traits is provided by a structure to forecast gastric cancer that was developed by fusing statistics and deep learning approaches. With the use of this framework, early identification of stomach cancer is considerably simpler.