The Transactions of The Korean Institute of Electrical Engineers (전기학회논문지)

The Korean Institute of Electrical Engineers (대한전기학회)
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Deep Learning based Computer-aided Diagnosis System for Gastric Lesion using Endoscope

위 내시경 영상을 이용한 병변 진단을 위한 딥러닝 기반 컴퓨터 보조 진단 시스템

  • Kim, Dong-hyun (Interdisciplinary Graduate Program for BIT Medical Convergence, Kangwon National University) ;
  • Cho, Hyun-chong (Dept. of Electronic Engineering and Interdisciplinary Graduate Program for BIT Medical Convergence, Kangwon National University)
  • Received : 2018.06.04
  • Accepted : 2018.06.25
  • Published : 2018.07.01
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Abstract

Nowadays, gastropathy is a common disease. As endoscopic equipment are developed and used widely, it is possible to provide a large number of endoscopy images. Computer-aided Diagnosis (CADx) systems aim at helping physicians to identify possibly malignant abnormalities more accurately. In this paper, we present a CADx system to detect and classify the abnormalities of gastric lesions which include bleeding, ulcer, neuroendocrine tumor and cancer. We used an Inception module based deep learning model. And we used data augmentation for learning. Our preliminary results demonstrated promising potential for automatically labeled region of interest for endoscopy doctors to focus on abnormal lesions for subsequent targeted biopsy, with Az values of Receiver Operating Characteristic(ROC) curve was 0.83. The proposed CADx system showed reliable performance.

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References

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