… In this study, we developed and evaluated a convolutional neural network (CNN)-based …
gastric cancer correctly diagnosed by the convolution neural network (CNN). c, d Noncancerous …

… network structure and a DeepLabv3 structure was constructed and trained using 21,217
gastroendoscopic images of five gastric conditions, peptic ulcer (PU), early gastric cancer (EGC) …

… , we develop an AdaBoost-based multi-column convolutional neural network (MCNN) for
enhancing gastric cancer screening. Experimental results show that the proposed MCNN …

… into five categories: advanced gastric cancer, early gastric cancer, high grade dysplasia, …
The mean area under the curve (AUC) of the model for differentiating gastric cancer and …

… Transfer learning with convolutional neural network for early gastric cancer classification on
… early gastric cancer in endoscopic images using a transferring convolutional neural network. …

… of gastric cancer risk from X-ray images using the patch-based Convolutional Neural Net…
patch-based CNN which can accurately detect gastric cancer risk even though there is only the …

… We aimed to develop a deep convolutional neural network (… metastasis (OPM) in advanced
gastric cancer (AGC). A total of … of OPM in patients with gastric cancer. The DCNN model …

… combined with a convolutional neural network model can be used for the diagnosis of four
types of cancer including gastric cancer, colon cancer, rectal cancer, and lung cancer. Raman …

… Convolutional neural networks perform well in … convolutional neural network was used to
construct a framework for automatic tumor recognition based on enhanced CT images of gastric …

… apply a deep learning approach in a series of gastric cancer (… For each stain, a survival
convolutional neural network (CNN… to predict patch-based risks for cancer-specific death in a 10-…