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

Yejin Jeon; Eunbyul Cho; Sehwa Moon; Seung-Hoon Chae; Hae Young Jo; Tae Oh Kim; Chang Mo Moon; Jang-Hwan Choi

doi:10.1117/12.2522159

27 March 2019 Deep convolutional neural network-based automated lesion detection in wireless capsule endoscopy

Yejin Jeon, Eunbyul Cho, Sehwa Moon, Seung-Hoon Chae, Hae Young Jo, Tae Oh Kim, Chang Mo Moon, Jang-Hwan Choi

Proceedings Volume 11050, International Forum on Medical Imaging in Asia 2019; 110501N (2019) https://doi.org/10.1117/12.2522159
Event: 2019 Joint International Workshop on Advanced Image Technology (IWAIT) and International Forum on Medical Imaging in Asia (IFMIA), 2019, Singapore, Singapore

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.

Citation Download Citation

Yejin Jeon, Eunbyul Cho, Sehwa Moon, Seung-Hoon Chae, Hae Young Jo, Tae Oh Kim, Chang Mo Moon, and Jang-Hwan Choi "Deep convolutional neural network-based automated lesion detection in wireless capsule endoscopy", Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 110501N (27 March 2019); https://doi.org/10.1117/12.2522159

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