… , convolutional neural networks play an important role in the identification of gastric cancer.
… current applications of convolutional neural networks in the gastric cancer identification. The …

… rate of advanced gastric cancer was high and that of early gastric cancer was low (18.6%).
Unlike these previous studies, our current study is limited to small early gastric cancer, which …

… To construct an AI-based diagnostic system, we used a deep neural network architecture
called the Single Shot MultiBox Detector (SSD, https://arxiv.org/abs/1512.02325), without …

… on the first row indicate the actual early gastric cancer regions. The images on the second
row represent the activated map extracted from the last convolutional layer of the network. …

… gastric mucosal lesions. However, the diagnostic efficacy of non-experts in differentiating
early gastric cancer … based on convolutional neural network (CNN) to analyze gastric mucosal …

… convolutional neural network-based automatic detection scheme to assist the diagnosis of
early gastric cancer … We performed transfer learning using two classes (cancer and normal) of …

… the invasion depth of gastric cancer and thus could be used to screen patients for endoscopic
resection. To evaluate the ability of a CNN to determine gastric cancer invasion depth, we …

… , to exclude tumor reflection, cases where tumorous lesions were present in the stomach.
Using the extracted development images mentioned above, we entered the gastric cancer and …

… This study aimed to evaluate the accuracy and effectiveness of the convolutional neural
network (CNN) in diagnosing gastric cancer and predicting the invasion depth of gastric cancer …

… Premalignant changes in the gastric mucosa are well-known risk factors for the … leading
to intestinal-type gastric carcinoma. In this multistep model of gastric carcinogenesis, H. pylori …