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Toward real-time polyp detection using fully CNNs for 2D Gaussian shapes prediction
- Source :
- Medical Image Analysis
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- To decrease colon polyp miss-rate during colonoscopy, a real-time detection system with high accuracy is needed. Recently, there have been many efforts to develop models for real-time polyp detection, but work is still required to develop real-time detection algorithms with reliable results. We use single-shot feed-forward fully convolutional neural networks (F-CNN) to develop an accurate real-time polyp detection system. F-CNNs are usually trained on binary masks for object segmentation. We propose the use of 2D Gaussian masks instead of binary masks to enable these models to detect different types of polyps more effectively and efficiently and reduce the number of false positives. The experimental results showed that the proposed 2D Gaussian masks are efficient for detection of flat and small polyps with unclear boundaries between background and polyp parts. The masks make a better training effect to discriminate polyps from the polyp-like false positives. The proposed method achieved state-of-the-art results on two polyp datasets. On the ETIS-LARIB dataset we achieved 86.54% recall, 86.12% precision, and 86.33% F1-score, and on the CVC-ColonDB we achieved 91% recall, 88.35% precision, and F1-score 89.65%. © 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
- Subjects :
- Computer science
Gaussian
Normal Distribution
Colonic Polyps
Binary number
Health Informatics
Convolutional neural network
030218 nuclear medicine & medical imaging
Medical technology: 620 [VDP]
03 medical and health sciences
symbols.namesake
0302 clinical medicine
Kreft i tykktarm og endetarm
otorhinolaryngologic diseases
False positive paradox
medicine
Humans
Radiology, Nuclear Medicine and imaging
Segmentation
Medisinsk teknologi: 620 [VDP]
Radiological and Ultrasound Technology
business.industry
Deep learning
Pattern recognition
Colonoscopy
medicine.disease
Colorectal cancer
Computer Graphics and Computer-Aided Design
digestive system diseases
Colon polyps
symbols
Neural Networks, Computer
Computer Vision and Pattern Recognition
Artificial intelligence
business
Algorithms
030217 neurology & neurosurgery
Subjects
Details
- ISSN :
- 13618415
- Volume :
- 68
- Database :
- OpenAIRE
- Journal :
- Medical Image Analysis
- Accession number :
- edsair.doi.dedup.....dff9da82c296206069204a9ddcdc2f3b