Detection of cancer disease in any part of a human body is of utmost importance as it can be cured completely. In this research work, a prognosis of early gastric cancer detection by applying modern machine learning algorithms augmented with fast and efficient classification of white light images. In earlier studies for early gastric cancer detection schemes, nominal endoscopic images demand more computational effort, which slows down process and takes more time. Moreover, in the contemporary methodologies, only basic parameters were used to detect the symptoms of gastric cancer such as accuracy. Whilst in the proposed methodology, protein structure of the cancerous part is also examined with the help of Alpha fold software. A dataset consist of white-light-images is developed from the endoscopic images of the suspected patients. By utilitarian of this dataset in the proposed scheme, results are drawn which shows greater accuracy at a lower cost as compared to contemporary techniques
