A Deep Convolutional Neural Network Based Lung Disorder Diagnosis

Authors(4) :-J. Juditha Mercina, J. Madhumathi, V. Priyanga, M. Deva Priya

Lungs play an important role in human respiratory system. There are diseases that affect the functioning of lungs. To analyse lung diseases in the chest region using X-ray based Computer-Aided Diagnosis (CAD) system, it is necessary to determine the lung regions subject to analysis. In this paper, an intelligent system is proposed for lung disease detection. In this paper, Interstitial Lung Disease (ILD) patterns are classified using Convolutional Neural Networks (CNN). The proposed system involves five convolutional layers and three dense layers. The performance of the classification demonstrates the potential of CNN in analysing lung patterns.

Authors and Affiliations

J. Juditha Mercina
Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India
J. Madhumathi
Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India
V. Priyanga
Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India
M. Deva Priya
Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India

Convolution Neural Networks, X-Ray, Lung Diseases, Keras, Softmax Accuracy, Confidence, Confusion Matrix, Training, Testing

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Publication Details

Published in : Volume 5 | Issue 2 | March-April 2019
Date of Publication : 2019-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 102-112
Manuscript Number : CSEIT19525
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

J. Juditha Mercina, J. Madhumathi, V. Priyanga, M. Deva Priya, "A Deep Convolutional Neural Network Based Lung Disorder Diagnosis", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 2, pp.102-112, March-April-2019. Available at doi : https://doi.org/10.32628/CSEIT19525
Journal URL : http://ijsrcseit.com/CSEIT19525

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