An Ensemble-Based Deep Convolutional Neural Network for Computer-Aided Polyps Identification From Colonoscopy

Frontiers in Genetics

Saurav Mallik¹  Kangkana Bora²  Bunil Kumar Balabantaray³  Pallabi Sharma³  Kunio Kasugai⁴  Zhongming Zhao⁶ 

[1] Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States;Computer Science and Information Technology, Cotton University, Guwahati, India;Department of Computer Science and Engineering, National Institute of Technology Meghalaya, Shillong, India;Department of Gastroenterology, Aichi Medical University, Nagakute, Japan;Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States;MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, United States;

关键词: colorectal cancer;    deep learning;    polyp detection;    colonoscopy;    ensemble classifier;   

DOI  :  10.3389/fgene.2022.844391

来源: DOAJ

【 摘 要 】

Colorectal cancer (CRC) is the third leading cause of cancer death globally. Early detection and removal of precancerous polyps can significantly reduce the chance of CRC patient death. Currently, the polyp detection rate mainly depends on the skill and expertise of gastroenterologists. Over time, unidentified polyps can develop into cancer. Machine learning has recently emerged as a powerful method in assisting clinical diagnosis. Several classification models have been proposed to identify polyps, but their performance has not been comparable to an expert endoscopist yet. Here, we propose a multiple classifier consultation strategy to create an effective and powerful classifier for polyp identification. This strategy benefits from recent findings that different classification models can better learn and extract various information within the image. Therefore, our Ensemble classifier can derive a more consequential decision than each individual classifier. The extracted combined information inherits the ResNet’s advantage of residual connection, while it also extracts objects when covered by occlusions through depth-wise separable convolution layer of the Xception model. Here, we applied our strategy to still frames extracted from a colonoscopy video. It outperformed other state-of-the-art techniques with a performance measure greater than 95% in each of the algorithm parameters. Our method will help researchers and gastroenterologists develop clinically applicable, computational-guided tools for colonoscopy screening. It may be extended to other clinical diagnoses that rely on image.

【 授权许可】

Unknown