Open AccessDeep learning driven colorectal lesion detection in gastrointestinal endoscopic and pathological imaging
Author(s) -
Yuchen Cai,
Fangfen Dong,
Yiting Shi,
Liyuan Lu,
Chen Chen,
Ping Lin,
Yu Xue,
Jianhua Chen,
Su-Yu Chen,
Xiaomei Luo
Publication year - 2021
Publication title -
world journal of clinical cases
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.368
H-Index - 10
ISSN - 2307-8960
DOI - 10.12998/wjcc.v9.i31.9376
Subject(s) - medicine , colorectal cancer , endoscopy , pathological , mortality rate , cancer , incidence (geometry) , colorectal polyp , lesion , radiology , surgery , colonoscopy , physics , optics
Colorectal cancer has the second highest incidence of malignant tumors and is the fourth leading cause of cancer deaths in China. Early diagnosis and treatment of colorectal cancer will lead to an improvement in the 5-year survival rate, which will reduce medical costs. The current diagnostic methods for early colorectal cancer include excreta, blood, endoscopy, and computer-aided endoscopy. In this paper, research on image analysis and prediction of colorectal cancer lesions based on deep learning is reviewed with the goal of providing a reference for the early diagnosis of colorectal cancer lesions by combining computer technology, 3D modeling, 5G remote technology, endoscopic robot technology, and surgical navigation technology. The findings will supplement the research and provide insights to improve the cure rate and reduce the mortality of colorectal cancer.