Earlier Detection of Gastric Cancer Using Augmented Deep Learning Techniques in Big Data with Medical Iot (Miot)

Abstract

Patients with advanced stomach cancer typically experience occult peritoneal metastases, which is difficult to diagnose using the present technology. Pproposed model of this research detects early stages of Occult Peritoneal Metastasis in Gastric cancer. The early stage is along with metabolomics to explore biomarkers. When patient experience the early symptoms for Occult Peritoneal Metastasis in Gastric cancer, the initial diagnosis has been carried out. But manual prediction of this cancer could not detect the cancer so automatic diagnosis of the images by segmenting the preoperative computed tomography images by conditional Random fields along with Pro-DAE (Post processing Denoising Autoencoders)and the labeling in the images is removed by denoising filters and then the resulted images and the segmented images have undergone to the graph convolutional networks(GCN) and the result feature graph data has been undergone with the optimized classifier(Greywolf and cuckoo search naïve Bayes classifier )system has been used for earlier detection of cancer. By detecting the cancer at early stage can reduce the advance stages of cancer.