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Automatic colon polyp detection using region based deep CNN and post learning approaches

Shin, Younghak; Qadir, Hemin Ali Qadir; Aabakken, Lars; Bergsland, Jacob; Balasingham, Ilangko
Journal article, Peer reviewed
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URI
http://hdl.handle.net/11250/2582805
Date
2018
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  • Institutt for elektroniske systemer [1842]
  • Publikasjoner fra CRIStin - NTNU [26648]
Original version
IEEE Access. 2018, 6 40950-40962.   10.1109/ACCESS.2018.2856402
Abstract
Automatic image detection of colonic polyps is still an unsolved problem due to the large variation of polyps in terms of shape, texture, size, and color, and the existence of various polyp-like mimics during colonoscopy. In this paper, we apply a recent region-based convolutional neural network (CNN) approach for the automatic detection of polyps in the images and videos obtained from colonoscopy examinations. We use a deep-CNN model (Inception Resnet) as a transfer learning scheme in the detection system. To overcome the polyp detection obstacles and the small number of polyp images, we examine image augmentation strategies for training deep networks. We further propose two efficient post-learning methods, such as automatic false positive learning and offline learning, both of which can be incorporated with the region-based detection system for reliable polyp detection. Using the large size of colonoscopy databases, experimental results demonstrate that the suggested detection systems show better performance than other systems in the literature. Furthermore, we show improved detection performance using the proposed post-learning schemes for colonoscopy videos.
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Journal
IEEE Access

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