Multi-Classification of Polyps in Colonoscopy Images Based on an Improved Deep Convolutional Neural Network
Shuang Liu1,2,3, Xiao Liu1, Shilong Chang1, Yufeng Sun4, Kaiyuan Li1, Ya Hou1, Shiwei Wang1, Jie Meng5, Qingliang Zhao6, Sibei Wu1, Kun Yang1,2,3,*, Linyan Xue1,2,3,*
CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5837-5852, 2023, DOI:10.32604/cmc.2023.034720
- 29 April 2023
Abstract Achieving accurate classification of colorectal polyps during colonoscopy can avoid unnecessary endoscopic biopsy or resection. This study aimed to develop a deep learning model that can automatically classify colorectal polyps histologically on white-light and narrow-band imaging (NBI) colonoscopy images based on World Health Organization (WHO) and Workgroup serrAted polypS and Polyposis (WASP) classification criteria for colorectal polyps. White-light and NBI colonoscopy images of colorectal polyps exhibiting pathological results were firstly collected and classified into four categories: conventional adenoma, hyperplastic polyp, sessile serrated adenoma/polyp (SSAP) and normal, among which conventional adenoma could be further divided into… More >