Deep Learning-Based CNN Model for Early Detection and Classification of Colorectal Cancer

Abstract

The timely identification of colorectal cancer can greatly aid in the decision-making process and alleviate the burden on medical professionals. Histological and endoscopic image-based automation systems can be used to accomplish this. Recently, the success of deep learning has motivated the development of image- and video-based polyp identification and segmentation. Artificial intelligence techniques are being used in the majority of diagnostic colonoscopy rooms, and they are thought to be effective in predicting invasive malignancy. Preprocesses, picture patches, and designs based on convolutional neural networks are frequently utilized. Moreover, end-to-end learning and learning transfer methods have been used for detection and localization tasks, which decrease user reliance and increase accuracy with small datasets. Explainable deep networks, on the other hand, are favored because they offer clinical diagnoses with transparency, interpretability, reliability, and equity. In this review, we summarize the latest advances in such models, with or without transparency, for the prediction of colorectal cancer and also address the knowledge gap in the upcoming technology. The public availability of digital pathology datasets has made it possible to assess if using deep learning techniques to enhance the effectiveness and caliber of histologic diagnosis is feasible. This article proposes a model that uses the Convolutional Neural Network and Ranking algorithm to identify colorectal cancer. Based on the performance evaluation, it is found that the proposed model is yielding better results in diagnosis of Colorectal Cancer than the existing methods in terms of Recall, Precision and Accuracy.