An Ensemble-Based Deep Convolutional Neural Network for Computer-Aided Polyps Identification From Colonoscopy - Sharma, Pallabi; Balabantaray, Bunil Kumar; Bora, Kangkana; Mallik, Saurav; Kasugai, Kunio; Zhao, Zhongming (Frontiers in Genetics, 2022)
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, 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 powerful 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 Xception model. Here, we applied our strategy to still frames extracted from colonoscopy video. It outperformed the 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 to develop clinically applicable, computational-guided tools for colonoscopy screening. It may be extended to other clinical diagnosis that relies on image.