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Proceedings Paper

Ground-glass nodule classification with multiple 2.5-dimensional deep convolutional neural networks in chest CT images
Author(s): So Hyun Byun; Julip Jung; Helen Hong; Yong Sub Song; Hyungjin Kim; Chang Min Park
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Paper Abstract

The malignancy rate of GGN is different according to the presence and the size of a solid component. Thus, it is important to differentiate part-solid GGN with a variable sized solid component from pure GGN. In this paper, we propose a method of classifying the GGNs according to presence or size of solid component using multiple 2.5- dimensional deep CNNs. First, to consider not only intensity but also texture, and shape information, we propose an enhanced input image using image augmentation and removing background. Second, we proposed GGN-Net which can classify GGNs into three classes using multiple input images in chest CT images. Finally, we comparatively evaluate the classification performance according to different type of input images. In experiments, the accuracy of the proposed method using multiple input images was the highest at 82.76% and it was 10.35%, 13.79%, and 6.90% higher than that of using three single input image such as intensity-based, texture- and shape-enhanced input images, respectively.

Paper Details

Date Published: 27 March 2019
PDF: 6 pages
Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 110500O (27 March 2019); doi: 10.1117/12.2523715
Show Author Affiliations
So Hyun Byun, Seoul Women's Univ. (Korea, Republic of)
Julip Jung, Seoul Women's Univ. (Korea, Republic of)
Helen Hong, Seoul Women's Univ. (Korea, Republic of)
Yong Sub Song, Seoul National Univ. (Korea, Republic of)
Hyungjin Kim, Seoul National Univ. (Kosovo, Republic of)
Chang Min Park, Seoul National Univ. (Korea, Republic of)

Published in SPIE Proceedings Vol. 11050:
International Forum on Medical Imaging in Asia 2019
Feng Lin; Hiroshi Fujita; Jong Hyo Kim, Editor(s)

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