Published 2021-12-30
Abstract
Colon cancer is a common preventable cancer. Colon and rectum cancers rank among the top cancer types worldwide. The chances of survival increase with early diagnosis and treatment which can greatly increase the chances of eliminating the disease. This cancer occurs in the inner side of colon walls or the rectum walls in the large intestine. Most of these types of cancer begin as abnormal growth of tissue called as polyp. With the adoption of widespread colon cancer screening in the developed countries, the incidence of colon cancer has decreased in the targeted population. But the incidence and mortality of colorectal cancer (CRC) have been increasing over the last 25 years in the young adults below the age of 50. This cancer is common among men than women. A cancer diagnosis may be automated by using the power of Artificial Intelligence (AI), allowing us to evaluate more cases in less time and at a lower cost. In this research, CNN models are employed to analyses imaging data of colon cells. We utilize deep neural networks to develop prediction models for patient survival and conditional survival of colon cancer. Our models are trained and validated on data obtained from the Surveillance, Epidemiology and End Results Program. Image processing techniques are used by applying deep learning algorithm Convolution Neural Network (CNN) and the results are compared with classical machine learning algorithm. Hence, in the proposed method, there is significant accuracy improvement using deep learning algorithm compared to classical machine learning algorithms. It also provides the baseline for automated colon cancer diagnosis using Deep learning algorithms for further research.