Purpose: Gastric cancer remains the leading cause of cancer-related deaths in Northeast
Asia. Population-based endoscopic screenings in the region have yielded successful results …

Background Deep learning models have previously been established to predict the
histopathology and invasion depth of gastric lesions using endoscopic images. This study …

Background: Gastric cancer (GC), a significant health burden worldwide, is typically
diagnosed in the advanced stages due to its non-specific symptoms and complex …

Background Gastric cancer is a common kind of malignancies, with yearly occurrences
exceeding one million worldwide in 2017. Typically, ulcerous and cancerous tissues …

The early detection and accurate histopathological diagnosis of gastric cancer increase the
chances of successful treatment. The worldwide shortage of pathologists offers a unique …

Gastric cancer is the third-most-common cause of cancer-related deaths in the world.
Fortunately, it can be detected using endoscopy equipment. Computer-aided diagnosis …

Background Visual inspection, lesion detection, and differentiation between malignant and
benign features are key aspects of an endoscopist's role. The use of machine learning for …

Previous studies on deep learning (DL) applications in pathology have focused on
pathologist-versus-algorithm comparisons. However, DL will not replace the breadth and …

This study developed a new convolutional neural network model to detect and classify
gastric lesions as malignant, premalignant, and benign. We used 10,181 white-light …

Diagnosis and evaluation of early gastric cancer (EGC) using endoscopic images is
significantly important; however, it has some limitations. In several studies, the application of …