Deep Learning Impacts on Cancer Diagnosis - Theory, Method, and Applications

Abstract

Medical imaging permits visualization and quantitative studies of genetic improvement of biological processes that are of high importance for the early detection of cancer. In this research, we deliver a study on the aspects of cancer detection and diagnosis that leverage deep learning. Secondly, we also outline a general deep learning model for cancer detection. Third, we include a review and feedback on the latest work on deep learning systems for the identification and diagnosis of cancer and some future research directions. Deep learning has been widely used in the last couple of years in medical imaging science, as it is constrained by meritocratic abilities of high representative capability in visual evaluation and conventional machine learning methods. Deep learning as a wider model is suggested, requires less computer technology and allows a forecast with more reliable data volumes. This article gives the comparative study on deep learning impacts on Cancer Diagnosis. We describe aspects of deep cancer therapy for the first time in this document, including steps for cancer diagnosis with doctor-style phases. Applications and research guidelines are given in the last part of this manuscript which shows how deep learning models were successful for various types of cancer. A review and feedback on the latest work on deep learning systems for the identification and diagnosis of cancer and some future research directions.