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Separation of color channels from conventional colonoscopy images improves deep neural network detection of polyps
- Source :
- Journal of Biomedical Optics
- Publication Year :
- 2021
- Publisher :
- SPIE-Intl Soc Optical Eng, 2021.
-
Abstract
- Significance: Colorectal cancer incidence has decreased largely due to detection and removal of polyps. Computer-aided diagnosis development may improve on polyp detection and discrimination. Aim: To advance detection and discrimination using currently available commercial colonoscopy systems, we developed a deep neural network (DNN) separating the color channels from images acquired under narrow-band imaging (NBI) and white-light endoscopy (WLE). Approach: Images of normal colon mucosa and polyps from colonoscopies were studied. Each color image was extracted based on the color channel: red/green/blue. A multilayer DNN was trained using one-channel, two-channel, and full-color images. The trained DNN was then tested for performance in detection of polyps. Results: The DNN performed better using full-colored NBI over WLE images in the detection of polyps. Furthermore, the DNN performed better using the two-channel red + green images when compared to full-color WLE images. Conclusions: The separation of color channels from full-color NBI and WLE images taken from commercially available colonoscopes may improve the ability of the DNN to detect and discriminate polyps. Further studies are needed to better determine the color channels and combination of channels to include and exclude in DNN development for clinical use.
- Subjects :
- Paper
Computer science
Feature extraction
Biomedical Engineering
Colonic Polyps
Colonoscopy
colorectal cancer
01 natural sciences
Colonoscopes
010309 optics
Biomaterials
Narrow Band Imaging
0103 physical sciences
medicine
Humans
Diagnosis, Computer-Assisted
General
Contextual image classification
medicine.diagnostic_test
color channel separation
Color image
business.industry
Deep learning
polyp discrimination
deep learning
Pattern recognition
Image segmentation
Atomic and Molecular Physics, and Optics
Electronic, Optical and Magnetic Materials
RGB color model
Neural Networks, Computer
Artificial intelligence
artificial intelligence algorithms
business
narrow-band imaging
Subjects
Details
- ISSN :
- 10833668
- Volume :
- 26
- Database :
- OpenAIRE
- Journal :
- Journal of Biomedical Optics
- Accession number :
- edsair.doi.dedup.....91748eff943127e078f16677ede1561a