@inproceedings{157b63610ee845ebaed3f88ad223f23d,
title = "Deep convolutional neural network-based automated lesion detection in wireless capsule endoscopy",
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.",
keywords = "Convolutional neural Networks, Deep neural networks, Lesion detection, Small bowel tumor, Small-bowel Wireless Capsule Endoscopy, Wireless capsule endoscopy",
author = "Yejin Jeon and Eunbyul Cho and Sehwa Moon and Chae, {Seung Hoon} and Jo, {Hae Young} and Kim, {Tae Oh} and Moon, {Chang Mo} and Choi, {Jang Hwan}",
note = "Funding Information: This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government, MSIP (grant no: NRF-2017R1C1B5018287, NRF-2015M3A9A7029725, and NRF-2017M2A2A6A02070522, URL: http://nrf.re.kr). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Publisher Copyright: {\textcopyright} 2019 SPIE.; International Forum on Medical Imaging in Asia 2019 ; Conference date: 07-01-2019 Through 09-01-2019",
year = "2019",
doi = "10.1117/12.2522159",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Feng Lin and Hiroshi Fujita and Kim, {Jong Hyo}",
booktitle = "International Forum on Medical Imaging in Asia 2019",
}