Early diagnosis of colorectal cancer is considered a key factor in reducing the complications of the disease before it spreads throughout the body, which raises survival and increases the chance of patient recovery. This research introduces machine learning and deep learning classifiers trained on a publicly available histopathological dataset with four magnification levels (40×, 100×, 200×, 400×) and uses stratified k-fold cross-validation to classify colorectal cancer into five classes. Features are extracted in two ways: manually and automatically using a Convolutional Neural Network (CNN). Both machine learning and deep learning techniques performed well on the test data. In summary, machine learning using an automatic feature extraction method achieved an accuracy of 89%, Precision of 88%, Recall of 87%, and F1-Score of 87% using a Support Vector Machine (SVM) classifier, while machine learning using a manual feature extraction method achieved an accuracy, Precision, Recall, and F1-Score of 79% using an Extreme Gradient Boosting (XG-Boost) classifier at 200× magnification. On the other hand, the deep learning model ResNet-50 achieved the best result with 97% accuracy, Precision, Recall, and F1-Score using 100× magnification.
Al-Damen et al. (Thu,) studied this question.