Pneumonia detection in chest X-ray images using Convolutional Neural Network and fuzzy VIKOR

Yousofvand, Leila; Dowlatshahi, Mohammad Bagher; Pirdadeh Beiranvand, Mostafa

doi:10.22111/ijfs.2025.51603.9119

Document Type : Research Paper

Authors

¹ Department of Computer Engineering, Lorestan University, Khorramabad, 68135-1911, Lorestan, Iran

² Associate Professor, Computer Engineering, Lorestan University

https://doi.org/10.22111/ijfs.2025.51603.9119

Abstract

Pneumonia is a life-threatening respiratory disease that requires early and accurate diagnosis for effective treatment and reduced complications. However, conventional diagnostic methods such as PCR are often time-consuming, equipment-dependent, and limited to specialized medical centers. This study introduces a novel anomaly detection framework for pneumonia diagnosis using advanced machine learning techniques applied to chest X-ray images. To enhance classification performance, the framework integrates several feature selection methods, including Correlation-based Feature Selection (CFS) to evaluate feature relevance, Fisher Score to rank features based on discriminative power, Maximum Information Coefficient (MIC) to capture complex dependencies, and Local Learning-based Correlation Feature Selection (LLCFS) to improve accuracy by considering local feature correlations. To further enhance classification performance, this study introduces the first-ever application of Fuzzy VIKOR in pneumonia detection. This fuzzy logic-based ensemble method effectively handles uncertainty in medical imaging data, leading to more balanced decision-making when dealing with conflicting information. The proposed model was trained on a chest X-ray dataset and evaluated using key classification metrics, including accuracy, recall, precision, and F1-score. Experimental results confirm that the model outperforms baseline methods across all metrics, achieving an accuracy of 98.34%. These findings validate the effectiveness of the proposed framework and highlight its high potential for real-world deployment in AI-driven computer-aided diagnosis (CAD) systems, particularly in hospitals and telemedicine applications.

Keywords

Main Subjects

Computer Science

References

[1] A. Agnihotri, N. Kohli, Challenges, opportunities, and advances related to COVID-19 classification based on deep
learning, Data Science and Management, 6(2) (2023), 98-109. https://doi.org/10.1016/j.dsm.2023.03.005

[2] F. L. Chen, D. Z. Zhang, M. L. Han, VLP: A survey on vision-language pre-training, Machine Intelligence Research,
20(1) (2023), 38-56. https://doi.org/10.1007/s11633-022-1369-5

[3] S. Cheng, C. C. Pain, Y. K. Guo, R. Arcucci, Real-time updating of dynamic social networks for COVID-
19 vaccination strategies, Journal of Ambient Intelligence and Humanized Computing, 15(3) (2024), 1981-1994.
https://doi.org/10.1007/s12652-023-04589-7

[4] H. S. Chiang, D. H. Shih, B. Lin, M. H. Shih, An APN model for Arrhythmic beat classification, Bioinformatics,
30(12) (2014), 1739-1746. https://doi.org/10.1093/bioinformatics/btu101

[5] M. B. Dowlatshahi, A. Hashemi, Unsupervised feature selection: A fuzzy multi-criteria decision-making approach,
Iranian Journal of Fuzzy Systems, 20 (2023), 55-70. https://doi.org/10.22111/ijfs.2023.7630

[6] R. O. Duda, P. E. Hart, D. G. Stork, Pattern classification, Wiley, 2001, p. 654. https://www.worldcat.org/isbn/
9780471056690

[7] R. A. Fisher, The use of multiple measurements in taxonomic problems, Annals of Eugenics, 7(2) (1936), 179-188.
https://doi.org/10.1111/j.1469-1809.1936.tb02137.x

[8] R. Fusco, R. Grassi, V. Granata, S. V. Setola, F. Grassi, D. Cozzi, B. Pecori, F. Izzo, A. Petrillo, Artificial
intelligence and COVID-19 using chest CT scan and chest X-ray images: Machine learning and deep learning
approaches for diagnosis and treatment, Journal of Personalized Medicine, 11(10) (2021), 993. https://doi.org/
10.3390/jpm11100993

[9] T. Hafs, H. Zehir, A. Hafs, H. Brahmia, A. Nait-Ali, Enhancing recognition in multimodal biometric systems:
Score normalization and fusion of online signatures and fingerprints, Romanian Journal of Information Science and
Technology (ROMJIST), 27(1) (2024), 37-49. http://dx.doi.org/10.59277/ROMJIST.2024.1.03

[10] A. Hashemi, M. B. Dowlatshahi, S. Farshidi, P. Moradi, AE-MCDM: An autoencoder-based multi-criteria decisionmaking approach for unsupervised feature selection, Journal of Supercomputing, 81(804) (2025). https://doi.org/
10.1007/s11227-025-07316-5

[11] A. Hashemi, M. B. Dowlatshahi, H. Nezamabadi-Pour, MFS-MCDM: Multi-label feature selection using multicriteria
decision making, Knowledge-Based Systems, 206 (2020), 106365. https://doi.org/10.1016/j.knosys.
2020.106365

[12] A. Hashemi, M. B. Dowlatshahi, H. Nezamabadi-Pour, Ensemble of feature selection algorithms: A multi criteria
decision making approach, International Journal of Machine Learning and Cybernetics, 13(1) (2021), 49-69. https:
//doi.org/10.1007/s13042-021-01347-z

[13] A. Hashemi, M. B. Dowlatshahi, H. Nezamabadi-Pour, VMFS: A VIKOR-based multi-target feature selection,
Knowledge-Based Systems, 214 (2021), 106728. https://doi.org/10.1016/j.eswa.2021.115224

[14] A. Hashemi, M. Joodaki, N. Z. Joodaki, M. B. Dowlatshahi, Ant colony optimization equipped with an ensemble
of heuristics through multi-criteria decision making: A case study in ensemble feature selection, Applied Soft
Computing, (2022), 109046. https://doi.org/10.1016/j.asoc.2022.109046

[15] K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, IEEE Conference on Computer
Vision and Pattern Recognition (CVPR), (2016). https://doi.org/10.1109/CVPR.2016.90

[16] C. Ieracitano, N. Mammone, M. Versaci, G. Varone, A. R. Ali, A. Armentano, G. Calabrese, A. Ferrarelli, L.
Turano, C. Tebala, Z. Hussain, Z. Sheikh, A. Sheikh, G. Sceni, A. Hussain, F. C. Morabito, A fuzzy-enhanced deep
learning approach for early detection of Covid-19 pneumonia from portable chest X-ray images, Neurocomputing,
481 (2022), 202-215. https://doi.org/10.1016/j.neucom.2022.01.055

[17] A. M. Ismael, A. S¸eng¨ur, Deep learning approaches for COVID-19 detection based on chest X-ray images, Expert
Systems with Applications, 164 (2021), 114054. https://doi.org/10.1016/j.eswa.2020.114054

[18] D. Jaganathan, S. Balsubramaniam, V. Sureshkumar, S. Dhanasekaran, Concatenated modified LeNet approach for
classifying pneumonia images, Journal of Personalized Medicine, 14(3) (2024), 325. http://dx.doi.org/10.3390/
jpm14030328

[19] A. Kareem, H. Liu, P. Sant, Review on pneumonia image detection: A machine learning approach, Human-Centric
Intelligent Systems, 2(1-2) (2022), 31-43. https://doi.org/10.1007/s44230-022-00002-2

[20] I. Katsamenis, E. Protopapadakis, A. Voulodimos, A. Doulamis, N. Doulamis, Transfer learning for COVID-19
pneumonia detection and classification in chest X-ray images, in Proceedings of the 24th Pan-Hellenic Conference
on Informatics, (2021). https://doi.org/10.1145/3437120.3437300

[21] D. Kermany, K. Zhang, M. Goldbaum, Chest X-ray images (pneumonia), (2020). https://www.kaggle.com/
paultimothymooney/chest-xray-pneumonia

[22] M. Kim, C. Yan, D. Yang, Q. Wang, J. Ma, G. Wu, Deep learning in biomedical image analysis, Biomedical
Information Technology, (2020), 239-263. https://doi.org/10.1016/B978-0-12-816034-3.00008-0

[23] C. Liu, S. Cheng, M. Shi, A. Shah, W. Bai, R. Arcucci, IMITATE: Clinical prior guided hierarchical vision-language
pre-training, IEEE Transactions on Medical Imaging, 44 (2024), 519-529. https://doi.org/10.1109/TMI.2024.
3449690

[24] H. Liu, H. Motoda, Feature selection for knowledge discovery and data mining, Springer Science and Business
Media, 2012. https://doi.org/10.1007/978-1-4615-5689-3

[25] A. Majeed, X. Zhang, S. O. Hwang, Applications and challenges of federated learning paradigm in the big data era
with special emphasis on COVID-19, Big Data and Cognitive Computing, 6(4) (2022), 127. http://dx.doi.org/
10.3390/bdcc6040127

[26] X. Mei, H. C. Lee, K. Y. Diao, M. Huang, B. Lin, C. Liu, Z. Xie, Y. Ma, P. M. Robson, M. Chung, A. Bernheim,
V. Mani, C. Calcagno, K. Li, S. Li, H. Shan, J. Lv, T. Zhao, J. Xia, Q. Long, S. Steinberger, A. Jacobi, T. Deyer,
M. Luksza, F. Liu, B. P. Little, Z. A. Fayad, Y. Yang, Artificial intelligence–enabled rapid diagnosis of patients with
COVID-19, Nature Medicine, (2020). https://doi.org/10.1038/s41591-020-0931-3

[27] K. Michalak, H. Kwasnicka, Correlation based feature selection method, International Journal of Bio-Inspired
Computation, 2(5) (2010), 319-332. http://dx.doi.org/10.1504/IJBIC.2010.036158

[28] J. Mozaffari, A. Amirkhani, S. B. Shokouhi, A survey on deep learning models for detection of COVID-19, Neural
Computing and Applications, (2023), 1-29. https://doi.org/10.1007/s00521-023-08683-x

[29] M. T. Nafees, I. M. Rizwan, M. M. I. Khan, M. Farhan, A novel convolutional neural network for COVID-19
detection and classification using chest X-ray images, medRxiv, (2021). https://doi.org/10.1101/2021.08.11.
21261946

[30] A. Nandal, M. Blagojevic, D. Milosevic, A. Dhaka, L. N. Mishra, Fuzzy enhancement and deep hash layer based
neural network to detect Covid-19, Journal of Intelligent and Fuzzy Systems, 41 (2021), 1341-1351. http://dx.
doi.org/10.3233/JIFS-210222

[31] A. Narin, C. Kaya, Z. Pamuk, Automatic detection of coronavirus disease (COVID-19) using X-ray images and
deep convolutional neural networks, Pattern Analysis and Applications, 24(3) (2021), 1207-1220. https://doi.
org/10.1007/s10044-021-00984-y

[32] J. Ning, Neural network-based pattern recognition in the framework of edge computing, Romanian Journal of Information Science and Technology (ROMJIST), 27(1) (2024), 106-119. http://dx.doi.org/10.59277/ROMJIST.
2024.1.08

[33] S. Opricovic, Multicriteria optimization of civil engineering systems, PhD Thesis, Faculty of Civil Engineering,
Belgrade, 1998.

[34] T. Ozturk, M. Talo, E. A. Yildirim, U. B. Baloglu, O. Yildirim, U. R. Acharya, Automated detection of COVID-19
cases using deep neural networks with X-ray images, Computers in Biology and Medicine, 121 (2020), 103792.
https://doi.org/10.1016/j.compbiomed.2020.103792

[35] T. Rahman, M. E. H. Chowdhury, A. Khandakar, K. R. Islam, K. F. Islam, Z. B. Mahbub, M. A. Kadir, S. Kashem,
Transfer learning with deep convolutional neural network (CNN) for pneumonia detection using chest X-ray, Applied
Sciences, 10(9) (2020), 3233. http://dx.doi.org/10.3390/app10093233

[36] P. P. Rajpurkar, J. Irvin, K. Zhu, B. Yang, H. Mehta, T. Duan, D. Ding, A. Bagul, C. Langlotz, K. Shpanskaya,
M. P. Lungren, A. Y. Ng, CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning, in
arXiv:1711.05225, (2017). http://dx.doi.org/10.48550/arXiv.1711.05225

[37] C. H. Shin, J. J. Lee, C. Y. Jung, An enhanced algorithm for an optimal high-frequency emphasis filter based on
fuzzy logic for chest X-ray images, Journal of Information and Communication Convergence Engineering, 13(4)
(2015), 264-269. http://dx.doi.org/10.6109/jicce.2015.13.4.264

[38] K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, in arXiv preprint
arXiv:1409.1556, (2014). https://doi.org/10.48550/arXiv.1409.1556

[39] W. P. Sousa, C. C. P. Cruz, R. S. Lanzillotti, Fuzzy divergence for lung radiography image enhancement, Trends in
Computational and Applied Mathematics, 24(4) (2023), 699-716. http://dx.doi.org/10.5540/tcam.2023.024.
04.00699

[40] H. Zeng, Y. M. Cheung, Feature selection and kernel learning for local learning-based clustering, IEEE Transactions
on Pattern Analysis and Machine Intelligence, 33(8) (2011), 1532-1547. http://dx.doi.org/10.1109/TPAMI.2010.
215

Pneumonia detection in chest X-ray images using Convolutional Neural Network and fuzzy VIKOR

References

References

Volume 22, Issue 5September and October 2025Pages 159-179

Volume 22, Issue 5
September and October 2025
Pages 159-179