Novel Techniques for Classification of Lung Nodules using Deep Learning Approach



Bhavanishankar K1, *, M V Sudhamani1
1 Department of Computer Science and Engineering, RNS Institute of Technology, Bengaluru, India

Abstract

Objective:

Lung cancer is proving to be one of the deadliest diseases that is haunting mankind in recent years. Timely detection of the lung nodules would surely enhance the survival rate. This paper focusses on the classification of candidate lung nodules into nodules/non-nodules in a CT scan of the patient. A deep learning approach –autoencoder is used for the classification.

Investigation/Methodology:

Candidate lung nodule patches obtained as the results of the lung segmentation are considered as input to the autoencoder model. The ground truth data from the LIDC repository is prepared and is submitted to the autoencoder training module. After a series of experiments, it is decided to use 4-stacked autoencoder. The model is trained for over 600 LIDC cases and the trained module is tested for remaining data sets.

Results:

The results of the classification are evaluated with respect to performance measures such as sensitivity, specificity, and accuracy. The results obtained are also compared with other related works and the proposed approach was found to be better by 6.2% with respect to accuracy.

Conclusion:

In this paper, a deep learning approach –autoencoder has been used for the classification of candidate lung nodules into nodules/non-nodules. The performance of the proposed approach was evaluated with respect to sensitivity, specificity, and accuracy and the obtained values are 82.6%, 91.3%, and 87.0%, respectively. This result is then compared with existing related works and an improvement of 6.2% with respect to accuracy has been observed.

Keywords: Autoencoder, CAD, False positive reduction, Classification, Nodules, Lung.


Abstract Information


Identifiers and Pagination:

Year: 2019
Volume: 13
Publisher Item Identifier: EA-TOBEJ-2019-HT1-643-4

Article History:

Electronic publication date: 27/09/2019
Collection year: 2019

© 2019 K and Sudhamani.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


* Address correspondence to this author at the Department of Computer Science and Engineering, RNS Institute of Technology, Bengaluru, India;
E-mail: bsharsh@gmail.com