EVERGREEN

Joint Journal of Novel Carbon Resource Sciences and Green Asia Strategy

ISSN:2189-0420 (Print until Mar 2020)
ISSN:2432-5953 (Online)

SCImago Journal & Country Rank

Open Access
Scopus
Google Scholar
Crossref
SCImago Journal & Country Rank
4.3
2024CiteScore
 
69th percentile
Powered by Scopus
Metrics by SCOPUS 2024
CiteScore
4.3
SJR
0.391
SNIP
1.192

Design and Development of PSO-Firefly Hybrid Optimizer–CNN Model for Lung Disease Classification using Chest X-Ray Images

Tanu Dhiman1,*, Puneet Kumar1
1Department of Computer Science & Engineering, Chandigarh University, India
*Author to whom correspondence should be addressed:
E-mail: tanudhiman0707@gmail.com (TD)
Evergreen, Vol. 13, Iss. 01, pp. 280–293, 2026
DOI: 10.5109/7411069
Creative Commons Attribution 4.0 International Creative Commons Attribution 4.0 International
Received: May 02, 2025 | Revised: October 14, 2025 | Accepted: March 16, 2026 | Published: March 2026
Abstract
Lung diseases such as Pneumonia, COVID-19, and Tuberculosis (TB) are major contributors to global mortality, making early and accurate diagnosis crucial for effective treatment. This paper introduces a novel Hybrid Nature-Inspired Optimizer – Convolutional Neural Network (HNI-CNN) model for the classification of lung diseases from chest X-ray images. The model integrates Particle Swarm Optimization (PSO) and Firefly Algorithm (FFA) to form a hybrid feature selection method, which optimizes the extraction of relevant features, significantly enhancing the classification accuracy. Additionally, Principal Component Analysis (PCA) is employed to reduce dimensionality, ensuring the model focuses on the most informative features. Tested on the Kaggle lung disease dataset, the HNI-CNN model achieves state-of-the-art performance, with an accuracy of 98.0%, precision of 96.8%, and recall of 99.0%, outperforming leading models such as NasNetMobile, MobileNetV2, and EfficientNetB1. This research presents a significant advancement in intelligent diagnostic systems by offering a more reliable and efficient method for feature selection and classification. The hybrid optimization approach not only improves performance but also enhances the robustness of automated medical diagnostics, making it a valuable tool for expert systems in healthcare.
Keywords
Deep Learning; Hybrid Optimizer with nature-inspired component –convolutional neural network (HNI-CNN); Lung X-ray Images; Medical Imaging
Available Repositories
Journal DOI Zenodo Archive
Share Article
Article Metrics
--
Views
--
Downloads
--
Citations
Export Citation
BibTeX RIS Semantic Scholar CrossRef Mendeley Google Scholar
Full Text
Download PDF
References
  1. 1) Challenges and Issues
  2. 2) Handling Huge Image Size
  3. 3) Limited Available Datasets
  4. 4) High Correlation of Errors
  5. 5) Hybrid Feature Selection
  6. 6) Detection of Lung Chest X-Ray Images
  7. 7) Results and Performance Analysis
  8. 8) Mathematical Performance Evaluation
  9. 9) Mean Square Error Rate
  10. 10) Implementation Details
  11. 11) Visualization and Failure Analysis
  12. 12) Effectiveness of the Hybrid Optimization Strategy
  13. 13) Impact of PCA on Computational Efficiency
  14. 14) Improved Classification Performance
  15. 15) Interpretability Through Grad-CAM Visualizations
  16. 16) Analysis of Misclassifications
  17. 17) Clinical Applicability and Practical Value
  18. 18) Conclusion and Future Scope
  19. 19) Jasmine Pemeena Priyadarsini, M., Ketan Kotecha, G. K. Rajini, K. Hariharan, K. Utkarsh Raj, K. Bhargav Ram, V. Indragandhi, V. Subramaniyaswamy, and Sharnil Pandya. "Lung diseases detection using various deep learning algorithms." Journal of healthcare engineering, no. 1 (2023): 3563696 doi:10.1155/2023/3563696
  20. 20) Yuki, K., Fujiogi, M. and Koutsogiannaki, S. "COVID-19 pathophysiology: A review." Clinical immunology 215 (2020): 108427 doi:10.1016/j.clim.2020.108427
  21. 21) Talukder, Md Alamin, Md Abu Layek, Mohsin Kazi, Md Ashraf Uddin, and Sunil Aryal. "Empowering covid-19 detection: Optimizing performance through fine-tuned efficientnet deep learning architecture." Computers in Biology and Medicine 168 (2024) doi:10.1016/j.compbiomed.2023.107789
  22. 22) Bharati, S., Podder, P. and Mondal, M.R.H. "Hybrid deep learning for detecting lung diseases from X-ray images." Informatics in Medicine Unlocked 20 (2020):100391 doi:10.1016/j.imu.2020.100391
  23. 23) Khan, E., Rehman, M.Z.U., Ahmed, F., Alfouzan, F.A., Alzahrani, N.M. and Ahmad, J. "Chest X-ray classification for the detection of COVID-19 using deep learning techniques." Sensors 22, no. 3 (2022): 1211 doi:10.3390/s22031211
  24. 24) Akter, S., Shamrat, F.J.M., Chakraborty, S., Karim, A. and Azam, S.,. "COVID-19 detection using deep learning algorithm on chest X-ray images". Biology, 10 no. 11, (2021): 1174 doi:10.3390/biology10111174
  25. 25) Mahmud, T., Rahman, M.A. and Fattah, S.A. "CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization." Computers in biology and medicine 122 (2020): 103869 doi:10.1016/j.compbiomed.2020.103869
  26. 26) Ibrahim, A.U., Ozsoz, M., Serte, S., Al-Turjman, F. and Yakoi, P.S. "Pneumonia classification using deep learning from chest X-ray images during COVID-19". Cognitive computation, 16 no. 4 (2024): 1589-1601 doi:10.1007/s12559-020-09787-5
  27. 27) Li, L., Qin, L., Xu, Z., Yin, Y., Wang, X., Kong, B., Bai, J., Lu, Y., Fang, Z., Song, Q. and Cao, K. "Using artificial intelligence to detect COVID-19 and community-acquired pneumonia based on pulmonary CT: evaluation of the diagnostic accuracy". Radiology, 296 no. 2 (2020) :E65-E71 doi:10.1148/radiol.2020200905
  28. 28) Panwar, H., Gupta, P.K., Siddiqui, M.K., Morales-Menendez, R. and Singh, V. "Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet". Chaos, Solitons & Fractals, 138, (2020):109944 doi:10.1016/j.chaos.2020.109944
  29. 29) Hasan, M.K., Alam, M.A., Dahal, L., Roy, S., Wahid, S.R., Elahi, M.T.E., Martí, R. and Khanal, B. "Challenges of deep learning methods for COVID-19 detection using public datasets". Informatics in Medicine Unlocked, 30, (2022):100945 doi:10.1016/j.imu.2022.100945
  30. 30) Abbas, A., Abdelsamea, M.M. and Gaber, M.M. "Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network". Applied Intelligence, 51 (2021):854-864 doi:10.1007/s10489-020-01829-7
  31. 31) Ali, M., Shahroz, M., Akram, U., Mushtaq, M.F., Altamiranda, S.C., Obregon, S.A., Díez, I.D.L.T. and Ashraf, I. "Pneumonia detection using chest radiographs with novel efficientnetv2l model". IEEE Access, 12, (2024): 34691-707 doi:10.1109/ACCESS.2024.3372588
  32. 32) Bakır, H., Oktay, S., & Tabaru, E. "Detection of pneumonia from x-ray images using deep learning techniques". Journal of Scientific Reports-A, 052, (2023): 419-440 doi:10.59313/jsr-a.1219363
  33. 33) Makris, A., Kontopoulos, I. and Tserpes, K. "COVID-19 detection from chest X-Ray images using Deep Learning and Convolutional Neural Networks". In 11th hellenic conference on artificial intelligence(2020):60-66 doi:10.1145/3411408.3411416
  34. 34) Zheng, C., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H., Liu, W. and Wang, X. "Deep learning-based detection for COVID-19 from chest CT using weak label". IEEE Transactions on Medical Imaging, (2020):2020-03 doi:10.1109/TMI.2020.2995965
  35. 35) Ji, D., Zhang, Z., Zhao, Y. and Zhao, Q. "Research on classification of COVID‐19 chest X‐ray image modal feature fusion based on deep learning". Journal of Healthcare Engineering, no. 1 (2021): 6799202 doi:10.1155/2021/6799202
  36. 36) Minaee, S., Kafieh, R., Sonka, M., Yazdani, S., & Soufi, G. J. "Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning". Medical image analysis, no. 65, (2020):101794 doi:10.1016/j.media.2020.101794
  37. 37) Constantinou, M., Exarchos, T., Vrahatis, A. G., & Vlamos, P. "COVID-19 classification on chest X-ray images using deep learning methods". International Journal of Environmental Research and Public Health, 20 no. 3, (2023): 2035 doi:10.3390/ijerph20032035
  38. 38) Kundu, R., Das, R., Geem, Z.W., Han, G.T. and Sarkar, R., " Pneumonia detection in chest X-ray images using an ensemble of deep learning models". PloS one, 16 no. 9,(2021):e0256630 doi:10.1371/journal.pone.0256630
  39. 39) Ismael, Aras M., and Şengür, A. "Deep learning approaches for COVID-19 detection based on chest X-ray images." Expert Systems with Applications 164 (2021): 114054 doi:10.1016/j.eswa.2020.114054
  40. 40) Gupta, H., Bansal, N., Garg, S., Mallik, H., Prabha, A. and Yadav, J. "A hybrid convolutional neural network model to detect COVID‐19 and pneumonia using chest X‐ray images". International Journal of Imaging Systems and Technology, 33 no. 1, (2023): 39-52 doi:10.1002/ima.22829
  41. 41) Alqudah, A.M., Qazan, S. and Alqudah, A. "Automated systems for detection of COVID-19 using chest X-ray images and lightweight convolutional neural networks". Europe PMC, (2020) doi:10.21203/rs.3.rs-24305/v1
  42. 42) El Houby, E.M. "COVID‑19 detection from chest X-ray images using transfer learning". Scientific Reports, 14 no. 1,(2024):11639 doi:10.1038/s41598-024-61693-0
  43. 43) Heidari, M., Mirniaharikandehei, S., Khuzani, A.Z., Danala, G., Qiu, Y. and Zheng, B. "Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms". International journal of medical informatics, 144, (2020):104284 doi:10.1016/j.ijmedinf.2020.104284
  44. 44) Khan, A.I., Shah, J.L. and Bhat, M.M. "CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images". Computer methods and programs in biomedicine, 196, (2020):105581 doi:10.1016/j.cmpb.2020.105581
  45. 45) Ozturk, T., Talo, M., Yildirim, E.A., Baloglu, U.B., Yildirim, O. and Acharya, U.R. "Automated detection of COVID-19 cases using deep neural networks with X-ray images". Computers in biology and medicine, 121, (2020):103792 doi:10.1016/j.compbiomed.2020.103792
  46. 46) Dumakude, A. and Ezugwu, A.E. "Automated COVID-19 detection with convolutional neural networks". Scientific Reports, 13 no. 1, (2023): 10607 doi:10.1038/s41598-023-37743-4
  47. 47) Ucar, F. and Korkmaz, D. "COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images". Medical hypotheses, 140, (2020):109761 doi:10.1016/j.mehy.2020.109761
  48. 48) Apostolopoulos, I.D., Aznaouridis, S.I. and Tzani, M.A. "Extracting possibly representative COVID-19 biomarkers from X-ray images with deep learning approach and image data related to pulmonary diseases". Journal of Medical and Biological Engineering, 40, (2020):462-469 doi:10.1007/s40846-020-00529-4
  49. 49) Thangaraj, R., Pandiyan, P., Ramakrishnan, J., Nallakumar, R. and Eswaran, S. "A deep convolution neural network for automated covid-19 disease detection using chest x-ray images". Healthcare analytics, 4, (2023):100278 doi:10.1016/j.health.2023.100278
  50. 50) Kieu, S.T.H., Bade, A., Hijazi, M.H.A. and Kolivand, H. "A survey of deep learning for lung disease detection on medical images: state-of-the-art, taxonomy, issues and future directions". Journal of imaging, 6 no. 12, (2020):131 doi:10.3390/jimaging6120131
  51. 51) Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., Cai, M., Yang, J., Li, Y., Meng, X. and Xu, B. "A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)". European radiology, 31, (2021) : 6096-6104 doi:10.1101/2020.02.14.20023028
  52. 52) Chang, V., Mcwann, S., Hall, K., Xu, Q.A. and Ganatra, M.A. "Diagnosis of COVID-19 CT Scans Using Convolutional Neural Networks". SN Computer Science, 5 no. 5, (2024):625 doi:10.1007/s42979-024-02878-2
  53. 53) Abdullah, M., berhe Abrha, F., Kedir, B. and Tagesse, T.T. "A Hybrid Deep Learning CNN model for COVID-19 detection from chest X-rays". Heliyon, 10 no. 5. (2024) doi:10.1016/j.heliyon.2024.e26938
  54. 54) Kaur, P., Harnal, S., Tiwari, R., Alharithi, F.S., Almulihi, A.H., Noya, I.D. and Goyal, N. "A hybrid convolutional neural network model for diagnosis of COVID-19 using chest X-ray images". International Journal of Environmental Research and Public Health, 18 no. 22, (2021):12191 doi:10.3390/ijerph182212191
  55. 55) Mousavi, Z., Shahini, N., Sheykhivand, S., Mojtahedi, S. and Arshadi, A. "COVID-19 detection using chest X-ray images based on a developed deep neural network". SLAS technology, 27 no. 1 (2022):63-75 doi:10.1016/j.slast.2021.10.011
  56. 56) Phung VH, Rhee EJ. A High-Accuracy Model Average Ensemble of Convolutional Neural Networks for Classification of Cloud Image Patches on Small Datasets. Applied Sciences. 2019; 9(21):4500 doi:10.3390/app9214500
Other Papers in This Issue