Pneumonia Detection on Chest X-ray Images Using Hybrid Convolution Neural Networks

Authors

  • Pradeep Rao K.B Dept. of Computer Science and Engineering, Sri Dharmasthala Manjunatheshwara Institute of Technology, Ujire-574240, India https://orcid.org/0000-0002-1037-9822
  • Manoj T. Gadiyar H Dept. of Computer Science and Engineering, Sri Dharmasthala Manjunatheshwara Institute of Technology, Ujire-574240, India
  • Guruprasad Dept. of Computer Science and Engineering, Sri Dharmasthala Manjunatheshwara Institute of Technology, Ujire-574240, India https://orcid.org/0000-0002-1037-9822
  • Basavaraj N Dept. of Computer Science and Engineering, Sri Dharmasthala Manjunatheshwara Institute of Technology, Ujire-574240, India
  • Dhanush T.M Dept. of Computer Science and Engineering, Sri Dharmasthala Manjunatheshwara Institute of Technology, Ujire-574240, India
  • Madhukumara M Dept. of Computer Science and Engineering, Sri Dharmasthala Manjunatheshwara Institute of Technology, Ujire-574240, India

DOI:

https://doi.org/10.26438/ijcse/v11i5.15

Keywords:

neumonia, Chest X-ray,, VGG16, VGG19, Ensemble Classifier,, Convolutional Neural Network

Abstract

Pneumonia primarily affects individuals who are either older than 65 years or younger than five years. Timely identification and prompt treatment of pneumonia can significantly improve the chances of survival for individuals. Pneumonia detection often involves extensive analysis of Chest X-ray images. Recent research indicates that the utilization of deep learning technique holds significant promise in the accurate identification and diagnosis of pneumonia. A novel approach is proposed in this research, where a hybrid Convolutional Neural Network is introduced for the purpose of pneumonia detection in chest X-ray images. In this approach, initially images of Chest X-ray are gathered and preprocessed. Later feature extraction was done using VGG16 and VGG19 model. After training and testing Machine Learning (ML) classifiers, an ensemble classifier was created for classification of pneumonia. Experiment results shows that ensemble classifier outperforms existing state of art methods by exhibiting superior accuracy and recall performance.

References

[1] R Jain, P Nagrath, G Kataria, V S Kaushik, D J Hemanth, “Pneumonia detection in chest X-ray images using convolutional neural networks and transfer learning,” Measurement,Vol.165,2020.

[2] Chouhan V, Singh S K, Khamparia A, Gupta D, Tiwari P, Moreira C, De Albuquerque,” A novel transfer learning based approach for pneumonia detection in chest X-ray images”, Applied Sciences, Vol. 10, Issue 2, pp. 1-17,2020.

[3] Sharma H, Jain JS, Bansal P, Gupta S, “Feature Extraction and Classification of Chest X-ray images Using CNN to Detect Pneumonia,”International Conference on Cloud Computing, Data Science & Engineering , pp.227-231,2020 .

[4] Rahman T, Chowdhury ME, Khandakar A, Islam KR, Islam KF, Mahbub ZB, Kadir MA, Kashem S , “Transfer learning with deep convolutional neural network (CNN) for pneumonia detection using chest X-ray,” Applied Sciences, Vol. 10, Issue 9, pp. 1-17,2020. https://doi.org/10.3390/app10093233

[5] Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T, Ding D, Bagul A, Langlotz C, Shpanskaya K, Lungren MP, “Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning,” pp. 1-7,2017.

[6] Stephen O, Sain M, Maduh UJ, Jeong DU, “An efficient deep learning approach to pneumonia classification in healthcare,” Journal of healthcare engineering, Vol. 2019, pp. 1-7, 2019.

[7] Wu H, Xie P, Zhang H, Li D, Cheng M , “Predict pneumonia with chest X-ray images based on convolutional deep neural learning networks,” Journal of Intelligent & Fuzzy Systems, Vol. 39, Issue 3, pp. 2893-2907, 2020. 10.3233/JIFS-191438

[8] Kermany D, Zhang K, Goldbaum M, “Labeled optical coherence tomography (oct) and chest x-ray images for classification,” pp. 651, 2018.

[9] Yaseliani M, Hamadani AZ, Maghsoodi AI, Mosavi A., “Pneumonia Detection Proposing a Hybrid Deep Convolutional Neural Network Based on Two Parallel Visual Geometry Group Architectures and Machine Learning Classifiers,” Vol. 10,pp.62110-62128,2022.

[10] Mabrouk A, Díaz Redondo RP, Dahou A, Abd Elaziz M, Kayed M,. “Pneumonia Detection on Chest X-ray Images Using Ensemble of Deep Convolutional Neural Networks,” Applied Science, Vol. 12, Issue 13, pp. 1-15, 2022. https://doi.org/10.3390/app12136448

[11] Militante S V, Dionisio N V, Sibbaluca B G, “Pneumonia and COVID-19 detection using convolutional neural networks,” International Conference on Vocational Education and Electrical Engineering, pp. 1-6,2020.

[12] Sirish Kaushik V, Nayyar A, Kataria G, Jain R, “Pneumonia detection using convolutional neural networks (CNNs),” International Conference on Computing, Communications, and Cyber-Security, pp. 471-483,2020.

[13] Bibek Ranjan Ghosh, Siddhartha Banerjee, Ayush Nandi, Arya Panja, Pritam Mondal, Arunava Das, “Automated Covid-19 Detection System with CNN using Chest X-Ray and CT-Scans,” International Journal of Computer Engineering, Vol.10, Issue.5, pp.47-52, 2022.

[14] Amanze B C, Asogwa D C, Chukwuneke C I, “An Android Mobile Expert System for the Diagnosi of Pneumonia with Object-Oriented Methodology,” International Journal of Computer Engineering, Vol.7, Issue.4, pp.745-758, 2019.

Downloads

Published

2023-05-31
CITATION
DOI: 10.26438/ijcse/v11i5.15
Published: 2023-05-31

How to Cite

[1]
K. Pradeep Rao, H. Manoj T. Gadiyar, G. Guruprasad, N. Basavaraj, T. Dhanush, and M. Madhukumara, “Pneumonia Detection on Chest X-ray Images Using Hybrid Convolution Neural Networks”, Int. J. Comp. Sci. Eng., vol. 11, no. 5, pp. 1–5, May 2023.

Issue

Vol. 11 No. 5 (2023): IJCSE May Edition

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.