Paper Info
Title
Application of deep learning for autonomous detection and localization of colorectal polyps in wireless colon capsule endoscopy.
Abstract
Recent advances in deep learning have prompted a surge of interest in analysis of medical images. In this study, we developed a convolutional neural network (CNN) for autonomous detection of colorectal polyps, in images captured during wireless colon capsule endoscopy, with risk of malignant evolution to colorectal cancer. Our CNN is an improved version of ZF-Net which uses a combination of transfer learning, pre-processing and data augmentation. We further deployed our CNN as the basis for a Faster R-CNN to localize regions of images containing colorectal polyps. We created an image database of 11,300 capsule endoscopy images from a screening population, including colorectal polyps (any size or morphology, N=4800) and normal mucosa (N=6500). Our CNN scored an accuracy of 98.0%, a sensitivity of 98.1% and a specificity of 96.3%. Our network outperforms all state-of-the-art results in autonomous detection of colorectal polyps and shows high interpretability in terms of sensitive regions.
Year
DOI
Venue
2020
10.1016/j.compeleceng.2019.106531
Computers & Electrical Engineering
Keywords
Field
DocType
Colorectal polyps,Colon capsule endoscopy (CCE),Deep learning,Machine learning,Convolutional neural networks
Wireless,Computer science,Computer network,Artificial intelligence,Deep learning,Capsule endoscopy,Multimedia
Journal
Volume
ISSN
Citations 
81
0045-7906
0
PageRank 
References 
Authors
0.34
0
9
Name
Order
Citations
PageRank
Esmaeil S. Nadimi195.90
Maria M. Buijs200.34
Jürgen Herp300.34
Rasmus Krøijer400.34
Morten Kobaek-Larsen500.34
Emilie Nielsen600.34
Claus Duedal Pedersen700.34
Victoria Blanes-Vidal863.46
Gunnar Baatrup900.34