Review of Application of Convolutional Neural Network in Auxiliary Diagnosis of Colorectal Polyps

Abstract

Colorectal cancer is a malignant tumor that mainly occurs in the tissues of the colon and rectum, and its early detection and treatment are of great significance. The early detection and prevention of colorectal cancer mainly involves visual examination of the patient??s intestines to screen for colorectal polyps, but manual examination has the disadvantage of high misdiagnosis rate. The auxiliary diagnostic system based on convolutional neural networks (CNN) has shown the most advanced performance in the diagnosis of colorectal polyps, and is currently a research hotspot in the field of computer-aided diagnosis. Based on important literature published in recent years, a systematic review of the application of convolutional neural networks in the auxiliary diagnosis of colorectal polyps is conducted. Firstly, the commonly used datasets in the field of colorectal polyp diagnosis are introduced, including image and video datasets. Secondly, the application of CNN in colorectal polyp detection, segmentation, and classification is systematically elaborated. The main improvement ideas, advantages and disadvantages, and performance of each algorithm are analyzed in depth, aiming to provide researchers with a more systematic reference, and summarize the interpretability of deep learning models. Finally, a summary of various algorithms for assisting the diagnosis of colorectal polyps based on CNN is provided, and future research directions are prospected.

Keywords

• colorectal polyps; convolutional neural networks (cnn); computer aided diagnosis; interpretability