Deep Learning-Based Multi-state Colorectal Cancer Histological Image Classification

Colorectal carcinoma is one of the most prevalent cancers of intestinal tract. Globally, it is the second biggest reason in cancer-associated deaths. A swift diagnosis is required to enhance the lifespan of colon cancer patients. Nevertheless, disease categorization becomes challenging since histopathological images include a variety of cells and features. This research provides a deep learning-based model for colorectal cancer multi-state classification. Expert-level reliability in medical image categorization has lately been proven using deep learning methods. A deep learning model, Dense Net 121, is employed to classify the colorectal cancer in eight tissue types. Dense Net 121 is being trained and assessed at several epochs to measure the learning rate. The learning rate, confusion map, and accuracy rate are used to assess the effectiveness of the pretrained model. Dense Net 121 obtained 97% classification accuracy with an average CPU time of 31 s. This model will be able to detect and categorize tissue types from similar histology imaging datasets in the future.