Jiang Qianru, Yu Yulin, Ren Yipei, Li Sheng, He Xiongxiong
College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, Zhejiang, P.R. China.
Med Biol Eng Comput. 2025 Feb;63(2):293-320. doi: 10.1007/s11517-024-03203-y. Epub 2024 Sep 30.
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.
深度学习的最新进展显著改善了胃肠道疾病的智能分类，特别是在辅助临床诊断方面。本文旨在综述一种针对胃肠道疾病的计算机辅助诊断（CAD）系统，使其与实际临床诊断过程相一致。它全面调查了为胃肠道疾病分类量身定制的深度学习（DL）技术，解决了复杂场景中固有的挑战、临床限制以及胃肠道成像中遇到的技术障碍。首先，定位食管、胃、小肠和大肠，以确定病变所在的器官。其次，在已知图像对应器官位置的前提下，进行单一疾病的定位检测和分类。最后，对多种疾病进行综合分类。比较单分类和多分类的结果以实现更准确的分类结果，并进一步构建了一种更有效的胃肠道疾病计算机辅助诊断系统。
