| Title |
Deep Learning based Computer-aided Diagnosis System for Gastric Lesion using Endoscope |
| Authors |
김동현(Dong-hyun Kim) ; 조현종(Hyun-chong Cho) |
| DOI |
http://doi.org/10.5370/KIEE.2018.67.7.928 |
| Keywords |
Computer-aided Diagnosis(CADx) Systems ; Gastric lesions ; Endoscopy images ; Inception module |
| 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. |