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

Authors

Pallabi Sharma
Bunil Kumar Balabantaray
Kangkana Bora
Saurav Mallik
Kunio Kasugai
Zhongming Zhao

Publication Date

1-1-2022

Journal

Frontiers in Genetics

Abstract

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.

Keywords

colorectal cancer, deep learning, polyp detection, colonoscopy, ensemble classifier

DOI

10.3389/fgene.2022.844391

PMID

35559018

PMCID

PMC9086187

PubMedCentral® Posted Date

4-26-2022

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes