Detection of Gastric Cancer Using Big Data Analytics

Given its influence on global health, early detection and correct staging of stomach cancer are essential for improving patient outcomes. In order to improve the accuracy of stomach cancer detection and staging, this work explores an integrated strategy that combines radiomics characteristics, convolutional neural networks (CNN), and big data analytics. Our goal is to create a more sophisticated and accurate diagnostic model by utilising the combination of patient profiles, medical imaging data, and cutting-edge analytical techniques. This project is supported by a comprehensive analysis of the literature that covers machine learning applications in medical imaging, the function of big data analytics in healthcare, and earlier research on the diagnosis of stomach cancer. Proposed approach to data collecting, preprocessing, feature extraction, and model creation is explained in the methods section. We carefully go over datasets with various radiomics properties and relevant physiological parameters. Specifically, we use a variety of essential machine learning models—such as ensemble methods and deep learning strategies—to build stable staging and diagnostic frameworks.