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Journal of Healthcare Engineering
Volume 2017, Article ID 8314740, 7 pages
https://doi.org/10.1155/2017/8314740
Research Article

Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images

School of Computer Science & Software Engineering, Tianjin Polytechnics University, Tianjin, China

Correspondence should be addressed to Lei Zhao; moc.qq@7228596791

Received 10 March 2017; Accepted 14 May 2017; Published 9 August 2017

Academic Editor: Junfeng Gao

Copyright © 2017 QingZeng Song et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Lung cancer is the most common cancer that cannot be ignored and cause death with late health care. Currently, CT can be used to help doctors detect the lung cancer in the early stages. In many cases, the diagnosis of identifying the lung cancer depends on the experience of doctors, which may ignore some patients and cause some problems. Deep learning has been proved as a popular and powerful method in many medical imaging diagnosis areas. In this paper, three types of deep neural networks (e.g., CNN, DNN, and SAE) are designed for lung cancer calcification. Those networks are applied to the CT image classification task with some modification for the benign and malignant lung nodules. Those networks were evaluated on the LIDC-IDRI database. The experimental results show that the CNN network archived the best performance with an accuracy of 84.15%, sensitivity of 83.96%, and specificity of 84.32%, which has the best result among the three networks.