Colon Cancer Tissue Classification Using ML

In this paper, the classification of colon cancer tissues by means of machine learning approaches is evaluated. In today's world, a revolutionary advancement has come in the classification and diagnosis of diseases in the medical and healthcare sectors. Deep learning classifiers and machine learning methods are now broadly applied to accurately diagnose a number of diseases. Cancer is one of the world's most significant roots of death, appealing to the lives of one person out of every six. As per the national library of medicine, the third leading cause of death worldwide is colorectal cancer. Identifying an illness at a premature stage increases the chances of survival. Automated diagnosis and the classification of tissues from images can be completed much more quickly with the use of artificial intelligence. A publicly available IoT dataset CRC–VAL–HE–7K consisting of 7180 images, distributed among nine types of colorectal tissues: background, lymphocytes, adipose, mucus, colorectal adenocarcinoma epithelium, normal colon mucosa, debris, cancer-associated stroma, and, smooth muscle is used after preprocessing. Feature extraction is done by applying Differential-Box-Count on all blocks of images. The dataset is evaluated by these Machine Learning (ML) procedures: K-Nearest Neighbor, Support Vector Machine, Decision Tree, Random Forest, Extreme Gradient Boosting, and Gaussian Naive Bayes. Results show that the performance of Extreme Gradient Boosting is the best and most viable approach.