Multiclass Classification of Gastrointestinal Colorectal Cancer Using Deep Learning

Gastrointestinal diseases are increasing at a fast rate. Some of these lead to colorectal cancer. The presence of polyps in the large intestine may lead to colorectal cancer in later stages. Early detection and prediction of colorectal cancer is very crucial as it is the third most occurring cancer in the world. In this study, different deep learning methods for image classification were implemented to classify various gastrointestinal diseases including polyps detection. The ResNet50 model implemented with transfer learning achieved classification accuracy of 99.25% on training set. The EfficientNet model achieved classification accuracy of 93.25% on validation set and 94.75% on test set.