An efficient method to predict pneumonia from chest X-rays using deep learning approach

Uzair Shah; Alaa Abd-Alrazeq; Tanvir Alam; Mowafa Househ; Zubair Shah

doi:10.3233/SHTI200594

An efficient method to predict pneumonia from chest X-rays using deep learning approach

Uzair Shah, Alaa Abd-Alrazeq, Tanvir Alam, Mowafa Househ, Zubair Shah^*

^*Corresponding author for this work

CSE Information & Computing Technology

Hamad bin Khalifa University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

18 Citations (Scopus)

Abstract

Pneumonia is a severe health problem causing millions of deaths every year. The aim of this study was to develop an advanced deep learning-based architecture to detect pneumonia using chest X-ray images. We utilized a convolutional neural network (CNN) based on VGG16 architecture consisting of 16 fully connected convolutional layers. A total of 5856 high-resolution frontal view chest X-ray images were used for training, validating, and testing the model. The model achieved an accuracy of 96.6%, sensitivity of 98.1%, specificity of 92.4%, precision of 97.2%, and a F1 Score of 97.6%. This indicates that the model has an excellent performance in classifying pneumonia cases and normal cases. We believe, the proposed model will reduce physician workload, expand the performance of pneumonia screening programs, and improve healthcare service.

Original language	English
Title of host publication	THE IMPORTANCE OF HEALTH INFORMATICS IN PUBLIC HEALTH DURING A PANDEMIC
Editors	John Mantas, Arie Hasman, Mowafa S. Househ, Parisis Gallos, Emmanouil Zoulias
Publisher	IOS Press
Pages	457-460
Number of pages	4
ISBN (Electronic)	9781643680927
DOIs	https://doi.org/10.3233/SHTI200594
Publication status	Published - 2020

Publication series

Name	Studies in Health Technology and Informatics
Volume	272
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Keywords

Deep learning
convolutional neural network
pneumonia detection

Access to Document

10.3233/SHTI200594

Cite this

Shah, U., Abd-Alrazeq, A., Alam, T., Househ, M., & Shah, Z. (2020). An efficient method to predict pneumonia from chest X-rays using deep learning approach. In J. Mantas, A. Hasman, M. S. Househ, P. Gallos, & E. Zoulias (Eds.), THE IMPORTANCE OF HEALTH INFORMATICS IN PUBLIC HEALTH DURING A PANDEMIC (pp. 457-460). (Studies in Health Technology and Informatics; Vol. 272). IOS Press. https://doi.org/10.3233/SHTI200594

Shah, Uzair ; Abd-Alrazeq, Alaa ; Alam, Tanvir et al. / An efficient method to predict pneumonia from chest X-rays using deep learning approach. THE IMPORTANCE OF HEALTH INFORMATICS IN PUBLIC HEALTH DURING A PANDEMIC. editor / John Mantas ; Arie Hasman ; Mowafa S. Househ ; Parisis Gallos ; Emmanouil Zoulias. IOS Press, 2020. pp. 457-460 (Studies in Health Technology and Informatics).

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title = "An efficient method to predict pneumonia from chest X-rays using deep learning approach",

abstract = "Pneumonia is a severe health problem causing millions of deaths every year. The aim of this study was to develop an advanced deep learning-based architecture to detect pneumonia using chest X-ray images. We utilized a convolutional neural network (CNN) based on VGG16 architecture consisting of 16 fully connected convolutional layers. A total of 5856 high-resolution frontal view chest X-ray images were used for training, validating, and testing the model. The model achieved an accuracy of 96.6%, sensitivity of 98.1%, specificity of 92.4%, precision of 97.2%, and a F1 Score of 97.6%. This indicates that the model has an excellent performance in classifying pneumonia cases and normal cases. We believe, the proposed model will reduce physician workload, expand the performance of pneumonia screening programs, and improve healthcare service.",

keywords = "Deep learning, convolutional neural network, pneumonia detection",

author = "Uzair Shah and Alaa Abd-Alrazeq and Tanvir Alam and Mowafa Househ and Zubair Shah",

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doi = "10.3233/SHTI200594",

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publisher = "IOS Press",

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editor = "John Mantas and Arie Hasman and Househ, {Mowafa S.} and Parisis Gallos and Emmanouil Zoulias",

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Shah, U, Abd-Alrazeq, A, Alam, T , Househ, M & Shah, Z 2020, An efficient method to predict pneumonia from chest X-rays using deep learning approach. in J Mantas, A Hasman, MS Househ, P Gallos & E Zoulias (eds), THE IMPORTANCE OF HEALTH INFORMATICS IN PUBLIC HEALTH DURING A PANDEMIC. Studies in Health Technology and Informatics, vol. 272, IOS Press, pp. 457-460. https://doi.org/10.3233/SHTI200594

An efficient method to predict pneumonia from chest X-rays using deep learning approach. / Shah, Uzair; Abd-Alrazeq, Alaa; Alam, Tanvir et al.
THE IMPORTANCE OF HEALTH INFORMATICS IN PUBLIC HEALTH DURING A PANDEMIC. ed. / John Mantas; Arie Hasman; Mowafa S. Househ; Parisis Gallos; Emmanouil Zoulias. IOS Press, 2020. p. 457-460 (Studies in Health Technology and Informatics; Vol. 272).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - An efficient method to predict pneumonia from chest X-rays using deep learning approach

AU - Shah, Uzair

AU - Abd-Alrazeq, Alaa

AU - Alam, Tanvir

AU - Househ, Mowafa

AU - Shah, Zubair

PY - 2020

Y1 - 2020

N2 - Pneumonia is a severe health problem causing millions of deaths every year. The aim of this study was to develop an advanced deep learning-based architecture to detect pneumonia using chest X-ray images. We utilized a convolutional neural network (CNN) based on VGG16 architecture consisting of 16 fully connected convolutional layers. A total of 5856 high-resolution frontal view chest X-ray images were used for training, validating, and testing the model. The model achieved an accuracy of 96.6%, sensitivity of 98.1%, specificity of 92.4%, precision of 97.2%, and a F1 Score of 97.6%. This indicates that the model has an excellent performance in classifying pneumonia cases and normal cases. We believe, the proposed model will reduce physician workload, expand the performance of pneumonia screening programs, and improve healthcare service.

AB - Pneumonia is a severe health problem causing millions of deaths every year. The aim of this study was to develop an advanced deep learning-based architecture to detect pneumonia using chest X-ray images. We utilized a convolutional neural network (CNN) based on VGG16 architecture consisting of 16 fully connected convolutional layers. A total of 5856 high-resolution frontal view chest X-ray images were used for training, validating, and testing the model. The model achieved an accuracy of 96.6%, sensitivity of 98.1%, specificity of 92.4%, precision of 97.2%, and a F1 Score of 97.6%. This indicates that the model has an excellent performance in classifying pneumonia cases and normal cases. We believe, the proposed model will reduce physician workload, expand the performance of pneumonia screening programs, and improve healthcare service.

KW - Deep learning

KW - convolutional neural network

KW - pneumonia detection

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U2 - 10.3233/SHTI200594

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M3 - Conference contribution

C2 - 32604701

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T3 - Studies in Health Technology and Informatics

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BT - THE IMPORTANCE OF HEALTH INFORMATICS IN PUBLIC HEALTH DURING A PANDEMIC

A2 - Mantas, John

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A2 - Gallos, Parisis

A2 - Zoulias, Emmanouil

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ER -

Shah U, Abd-Alrazeq A, Alam T , Househ M , Shah Z. An efficient method to predict pneumonia from chest X-rays using deep learning approach. In Mantas J, Hasman A, Househ MS, Gallos P, Zoulias E, editors, THE IMPORTANCE OF HEALTH INFORMATICS IN PUBLIC HEALTH DURING A PANDEMIC. IOS Press. 2020. p. 457-460. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI200594

An efficient method to predict pneumonia from chest X-rays using deep learning approach

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