Towards Interpretable Deep Learning for Colon Cancer Diagnosis in Healthcare System: A Comprehensive Review

Abstract

Colon cancer is a life-threatening type of cancer, with a survival rate that is too low. This type of cancer is difficult to diagnose at an early stage due to slow and hidden growth. Blood in stools, abdominal pain, and chronic diarrhoea are indicators of colon cancer. Colonoscopy is utilised to detect colon-rectal cancer and polyps, which are prone to human error. This paper reviews recent studies on detecting colon cancer with the integration of deep learning and machine learning in healthcare. Histopathological images are fed into Mobile-Net, Two Stage CNN, Compact CNN Models, and Modified VGG-CNN+COATI OPTIMIZATION algorithm to extract features of affected cells. The literature available currently focuses on obtaining accuracy, sensitivity, efficiency, balanced performance, and better prediction with the aid of deep architecture. However, the existing work demonstrates better performance; it lacks interpretability, transparency(black-box issue), architectural simplicity and real-time validation. This paper describes how Machine learning, Deep learning, and hybrid methods have evolved in diagnosing colorectal cancer. Currently, explainable AI has emerged to interpret and diagnose colorectal tumours in healthcare, which would be a solution for the black box issue that will build trust and support, and better decision making as a future scope.