A Comparative Study for Methodologies and Algorithms Used In Colon Cancer Diagnoses and Detection

Abstract

Colon cancer is also referred to as colorectal cancer, a kind of cancer that starts with colon damage to the large intestine in the last section of the digestive tract. Elderly people typically suffer from colon cancer, but this may occur at any age.It normally starts as little, noncancerous (benign) mass of cells named polyps that structure within the colon. After a period of time these polyps can turn into advanced malignant tumors that attack the human body and some of these polyps can become colon cancers. So far, no concrete causes have been identified and the complete cancer treatment is very difficult to be detected by doctors in the medical field. Colon cancer often has no symptoms in early stage so detecting it at this stage is curable but colorectal cancer diagnosis in the final stages (stage IV), gives it the opportunity to spread to different pieces of the body, difficult to treat successfully, and the person's chances of survival are much lower. False diagnosis of colorectal cancer which mean wrong treatment for patients with long-term infections and they are suffering from colon cancer this causing the death for these patients. Also, the cancer treatment needs more time and a lot of money. This paper provides a comparative study for methodologies and algorithms used in colon cancer diagnoses and detection this can help for proposing a prediction for risk levels of colon cancer disease using CNN algorithm of the deep learning (Convolutional Neural Networks Algorithm).