Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images
Joint Authors
Song, QingZeng
Zhao, Lei
Luo, XingKe
Dou, XueChen
Source
Journal of Healthcare Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-08-09
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Song, QingZeng& Zhao, Lei& Luo, XingKe& Dou, XueChen. 2017. Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images. Journal of Healthcare Engineering،Vol. 2017, no. 2017, pp.1-7.
https://search.emarefa.net/detail/BIM-1181233
Modern Language Association (MLA)
Song, QingZeng…[et al.]. Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images. Journal of Healthcare Engineering No. 2017 (2017), pp.1-7.
https://search.emarefa.net/detail/BIM-1181233
American Medical Association (AMA)
Song, QingZeng& Zhao, Lei& Luo, XingKe& Dou, XueChen. Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images. Journal of Healthcare Engineering. 2017. Vol. 2017, no. 2017, pp.1-7.
https://search.emarefa.net/detail/BIM-1181233
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1181233