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A deep learning method for early screening of lung cancer
Author(s): Kunpeng Zhang; Huiqin Jiang; Ling Ma; Jianbo Gao; Xiaopeng Yang
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Paper Abstract

Lung cancer is the leading cause of cancer-related deaths among men. In this paper, we propose a pulmonary nodule detection method for early screening of lung cancer based on the improved AlexNet model. In order to maintain the same image quality as the existing B/S architecture PACS system, we convert the original CT image into JPEG format image by analyzing the DICOM file firstly. Secondly, in view of the large size and complex background of CT chest images, we design the convolution neural network on basis of AlexNet model and sparse convolution structure. At last we train our models on the software named DIGITS which is provided by NVIDIA. The main contribution of this paper is to apply the convolutional neural network for the early screening of lung cancer and improve the screening accuracy by combining the AlexNet model with the sparse convolution structure. We make a series of experiments on the chest CT images using the proposed method, of which the sensitivity and specificity indicates that the method presented in this paper can effectively improve the accuracy of early screening of lung cancer and it has certain clinical significance at the same time.

Paper Details

Date Published: 10 April 2018
PDF: 7 pages
Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106154Q (10 April 2018); doi: 10.1117/12.2303546
Show Author Affiliations
Kunpeng Zhang, Zhengzhou Univ. (China)
Huiqin Jiang, Zhengzhou Univ. (China)
Ling Ma, Zhengzhou Univ. (China)
Jianbo Gao, Zhengzhou Univ. (China)
Xiaopeng Yang, Zhengzhou Univ. (China)


Published in SPIE Proceedings Vol. 10615:
Ninth International Conference on Graphic and Image Processing (ICGIP 2017)
Hui Yu; Junyu Dong, Editor(s)

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