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논문 기본 정보

자료유형
학술대회자료
저자정보
Yuichiro Koizumi (Kyushu Institute of Technology) Noriaki MIYAKE (Kyushu Institute of Technology) Huimin Lu (Kyushu Institute of Technology) Hyoungseop Kim (Kyushu Institute of Technology) Seiichi MURAKAMI (University of Occupational and Environmental Health) Takatoshi AOKI (University of Occupational and Environmental Health) Shoji KIDO (Yamaguchi University)
저널정보
제어로봇시스템학회 제어로봇시스템학회 국제학술대회 논문집 ICCAS 2018
발행연도
2018.10
수록면
1,468 - 1,471 (4page)

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초록· 키워드

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In recent years, the proportion of deaths from cancer tends to increase in Japan, especially the number of deaths from lung cancer is increasing. CT device is effective for early detection of lung cancer. However, there is concern that an increase in burden on doctors will be caused by high performance of CT improving. Therefore, by presenting the “second opinion” by the CAD system, it reduces the burden on the doctor. In this paper, we develop a CAD system for automatic detection of lesion candidate regions such as lung nodules or ground glass opacity (GGO) from 3D CT images. Our proposed method consists of three steps. In the first step, lesion candidate regions are extracted using temporal subtraction technique. In the second step, the image is reconstructed by sparse coding for the extracted region. In the final step, 3D Convolutional Neural Network (3D-CNN) identification using reconstructed images is performed. We applied our method to 51 cases and True Positive rate (TP) of 79.81 % and False Positive rate (FP) of 37.65 % are obtained.

목차

Abstract
1. INTRODUCTION
2. PROPOSED METHOD
3. RESULTS
4. DISCUSSION
5. CONCLUSIONS
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UCI(KEPA) : I410-ECN-0101-2018-003-003540165