Early stage detection and classification of colon cancer using deep learning and explainable AI on histopathological images

Abstract

Colon cancer is one the most prominent and daunting life threatening illnesses in the world. Histopathological diagnosis is one of the most important factors in determining cancer type. The current study aims to create a computer-aided diagnosis system for differentiating tissue cells, benign colon tissues, and adenocarcinomas tissues of the colon, using convolutional neural networks and digital pathology images for such tumors. As a result, in the coming years, artificial intelligence will be a promising technology. The LC25000 dataset, which included 5000 photographs for each class, produced a total of 25000 digital images for lung and colonic cancer cells, as well as healthy cells. The photos of lung cancer were not included in our study because it was primarily focused on colon cancer. To categorize and classify the histopathological slides of adenocarcinomas and benign cells in the colon, a Convolutional neural network architecture was implemented. We also explored optimization techniques such as Explainable AI techniques, Lime and DeepLift to better understand the reasoning behind the decision the model arrived at. This allowed us to better understand and optimize our models for a more consistent accurate classification. Diagnosis validity of greater than 94% was obtained for colon distinguishing adenocarcinoma and benign colonic cells.