A Review on framework to Detect and Diagnose Body System based Cancer using deep Learning Techniques

Cancer imaging examination is based mainly on doctors’ manual interpretation, which requires professional abilities, clinical experience, and attentiveness. However, as the volume of medical imaging data grows, radiologists face increasing challenges. Artificial Intelligence (AI) detection of Digestive System Cancer (DSC) can provide a solution for automatic picture analysis and assist doctors in achieving high-precision intelligent cancer diagnosis. The primary purpose of this study is to explain the critical research methodologies for AI-based DSC detection, as well as to give meaningful learning and reference for relevant researchers. Meanwhile, it outlines the essential issues with these methodologies and provides better direction for future research. DSC automated categorization, recognition, and segmentation can be improved by utilizing machine learning and deep learning detection techniques, which reduce internal image information that humans find difficult to uncover. Using AI to assist imaging surgeons in diagnosing DSC for multiple organs and high incidence can achieve rapid and effective cancer identification while saving doctors’ diagnosis time. These can be the foundation for better clinical diagnosis, therapeutic planning, and quantitative DSC evaluation.