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Proceedings Paper

Deep convolutional neural network-based automated lesion detection in wireless capsule endoscopy
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Paper Abstract

Because most of the capsule-endoscopic images contain normal mucous membranes, physicians spend most of their reading time observing normal areas. Thus, a significant reduction in their reading time would be possible if only the portion of the image frame for which a particular lesion is suspected can be read intensively. This study aims to develop a deep convolutional neural-network-based model capable of automatically detecting lesions in the capsule-endoscopic images of a small bowel. The proposed model consists of two deep neural networks in parallel, each of which takes in images in RGB and CIELab color spaces, respectively. The neural-networks model is based on transfer-learned GoogLeNet architecture. Our proposed algorithm showed promising results in classifying endoscopic images where lesions exist (98.56% accuracy). If the proposed algorithm is used to screen abnormal images, it is expected to reduce a physician's reading time and to improve his/her reading accuracy.

Paper Details

Date Published: 27 March 2019
PDF: 5 pages
Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 110501N (27 March 2019); doi: 10.1117/12.2522159
Show Author Affiliations
Yejin Jeon, Ewha Womans Univ. (Korea, Republic of)
Eunbyul Cho, Ewha Womans Univ. (Korea, Republic of)
Sehwa Moon, Ewha Womans Univ. (Korea, Republic of)
Seung-Hoon Chae, Electronics and Telecommunications Research Institute (Korea, Republic of)
Hae Young Jo, Ewha Womans Univ. College of Medicine (Korea, Republic of)
Tae Oh Kim, Ewha Womans Univ. College of Medicine (Korea, Republic of)
Chang Mo Moon, Ewha Womans Univ. College of Medicine (Korea, Republic of)
Jang-Hwan Choi, Ewha Womans Univ. (Korea, Republic of)


Published in SPIE Proceedings Vol. 11050:
International Forum on Medical Imaging in Asia 2019
Feng Lin; Hiroshi Fujita; Jong Hyo Kim, Editor(s)

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