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논문 기본 정보

자료유형
학술대회자료
저자정보
Mitsuaki NAGAO (Kyushu Institute of Technology) Noriaki MIYAKE (Kyushu Institute of Technology) Yuriko YOSHINO (Kyushu Institute of Technology) Huimin LU (Kyushu Institute of Technology) Joo Kooi TAN (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) Yasushi HIRANO (Yamaguchi University) Shoji KIDO (Yamaguchi University)
저널정보
제어로봇시스템학회 제어로봇시스템학회 국제학술대회 논문집 ICCAS 2017
발행연도
2017.10
수록면
1,444 - 1,448 (5page)

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Cancer is a leading cause of death both in Japan and worldwide. Detection of cancer region in CT images is the most important task to early detection. Recently, visual screening based on CT images become useful tools for cancer detection. However, due to the large number of images and the complexity of the image processing algorithms, image processing technique is still required a high screening quality. To overcome this problem, some computer aided diagnosis (CAD) algorithms are proposed. In this paper, we have designed and developed a framework combining machine learning based on deep convolutional neural networks (DCNN) and temporal subtraction techniques based on non-rigid image registration algorithm. Our main classification method can be built into three main steps; i) pre-processing for image segmentation, ii) image matching for registration, and iii) classification of abnormal regions based on machine learning algorithms. We performed our proposed technique to 25 thoracic MDCT sets and obtained true positive rates of 92.31 [%], false positive rates of 6.32 [/case] were obtained.

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Abstract
1. INTRODUCTION
2. METHODS
3. EXPERIMENTAL RESULTS
4. CONCLUSIONS
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UCI(KEPA) : I410-ECN-0101-2018-003-001428039