Background The diagnosis of gastric cancer mainly relies on endoscopy, which is invasive
and costly. The aim of this study is to develop a predictive model for the diagnosis of gastric …

Non-invasive and cost-effective diagnosis of gastric cancer is essential to improve
outcomes. Aim of the study was to establish a neural network model based on patient …

Background Gastric cancer is one of the leading causes of death worldwide. Screening for
gastric cancer greatly relies on endoscopy and pathology biopsy, which are invasive and …

Background and Aim Stratifying gastric cancer (GC) risk and endoscopy findings in high‐risk
individuals may provide effective surveillance for GC. We developed a computerized image …

Background Although gastric cancer is a malignancy with high morbidity and mortality in
China, the survival rate of patients with early gastric cancer (EGC) is high after surgical …

Many researchers are trying hard to minimize the incidence of cancers, especially GC. For
GC, the five-year survival rate is generally 5–25%, but for EGC it can be reduced by up to …

This chapter mainly focuses on the application of digital image processing and machine
learning algorithms in order to detect the gastric cancer regions and to classify the normal …

Gastric cancer is characterized by the growth of cancerous cells within the lining of the
stomach. Traditionally, this condition has been challenging to diagnose. However, today …

Results The best training method was the Quick method, which had an accuracy of 81.82%.
The most important factors associated with tumor staging were age and polymorphisms of …

A comprehensive screening method using machine learning and many factors (biological
characteristics, Helicobacter pylori infection status, endoscopic findings and blood test …