CLELNet: A continual learning network for esophageal lesion analysis on endoscopic images

A review of deep learning methods for gastrointestinal diseases classification applied in computer-aided diagnosis system

Recent advancements in deep learning have significantly improved the intelligent classification of gastrointestinal (GI) diseases, particularly in aiding clinical diagnosis. This paper seeks to review a computer-aided diagnosis (CAD) system for GI diseases, aligning with the actual clinical diagnostic process. It offers a comprehensive survey of deep learning (DL) techniques tailored for classifying GI diseases, addressing challenges inherent in complex scenes, clinical constraints, and technical obstacles encountered in GI imaging. Firstly, the esophagus, stomach, small intestine, and large intestine were located to determine the organs where the lesions were located. Secondly, location detection and classification of a single disease are performed on the premise that the organ's location corresponding to the image is known. Finally, comprehensive classification for multiple diseases is carried out. The results of single and multi-classification are compared to achieve more accurate classification outcomes, and a more effective computer-aided diagnosis system for gastrointestinal diseases was further constructed.