International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Automated Monkeypox Classification Using ReliefF Feature Selection and Stacked Ensemble Learning with Optimized Performance Metrics

Wameedh Raad FathelMinistry of Education, General Directorate of Education in Nineveh, Iraq

Vol 9 No 11 (2025): Volume 9, Issue 11, November 2025 | Pages: 175-186

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 15-11-2025

doi Logo doi.org/10.47001/IRJIET/2025.911020

pdf iconFull Text PDF
Abstract

The accurate and in time detection of Monkeypox using medical images is crucial for effective disease management. In this paper an improved classified system that integrates texture, local binary pattern (LBP), and statistical features with advanced feature selection and ensemble learning has been proposed.  ReliefF algorithm was used as feature selection, maintaining the top 70%of features, and hyperparameter optimization has been applied to the Support Vector Machine (SVM) classifier. Additional algorithms: Random Forest, K-Nearest Neighbors (KNN), Logistic Regression, and a Stacked Ensemble model were also used as classifier. Different metrics like accuracy, precision, recall, and F1-score. The highest accuracy of 92.5%. Confusion matrix and the area under the Receiver Operating Characteristic (ROC) curve (AUC) visually demonstrate model performance. The overall results confirmed that integrating feature selection with ensemble learning can significantly improve the robustness and reliability of automated Monkeypox detection.

Keywords

Image Classification, Feature Selection, ReliefF, Ensemble Learning, Automated Detection


Citation of this Article

Wameedh Raad Fathel. (2025). Automated Monkeypox Classification Using ReliefF Feature Selection and Stacked Ensemble Learning with Optimized Performance Metrics. International Research Journal of Innovations in Engineering and Technology - IRJIET, 9(11), 175-186. Article DOI https://doi.org/10.47001/IRJIET/2025.911020

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
References
  1. C. Vega, R. Schneider, and V. Satagopam, “Analysis: Flawed datasets of monkeypox skin images,” J. Med. Syst., vol. 47, no. 1, p. 37, Feb. 2023. doi: 10.1007/s10916-023-01928-1.
  2. F. Uysal, “Detection of monkeypox disease from human skin images with a hybrid deep learning model,” Diagnostics, vol. 13, no. 10, p. 1772, Oct. 2023. doi: 10.3390/diagnostics13101772.
  3. N. Andino Maseleno, M. Huda, and C. Ratanamahatana, “An explainable deep learning approach for classifying monkeypox disease by leveraging skin lesion image data,” Emerg. Sci. J., vol. 8, no. 5, p. 013, 2024. doi: 10.28991/ESJ-2024-08-05-013.
  4. G. Argenziano and I. Zalaudek, Dermoscopy of pigmented skin lesions: A practical guide, Elsevier, 2016.
  5. A. Esteva et al., “Dermatologist-level classification of skin cancer with deep neural networks,” Nature, vol. 542, no. 7639, pp. 115–118, Oct. 2017. doi: 10.1038/nature21056.
  6. R. Kohavi and G. H. John, “Wrappers for feature subset selection,” Artificial Intelligence, vol. 97, no. 1–2, pp. 273–324, Nov. 1997. doi: 10.1016/S0004-3702(97)00043-3.
  7. B. Harangi, “Skin lesion classification with ensembles of deep convolutional neural networks,” J. Biomed. Inform., vol. 86, pp. 25–32, Oct. 2018. doi: 10.1016/j.jbi.2018.08.006.
  8. S. S. M. Sheet, M. M. Al-Hatab, and M. Qasim, “A novel method for examining promoters using statistical analysis and artificial intelligence learning,” IAES International Journal of Artificial Intelligence (IJ-AI), vol. 14, no. 5, pp. 4006–4016, Oct. 2025, doi: 10.11591/ijai.v14.i5.pp4006-4016.
  9. S. S. Mohammed Sheet, M. M. Al-Hatab, and M. Qasim, “New features developed for the detection of a promoter based on machine learning,” in Proc. 2025 4th Int. Conf. on Electronics Representation and Algorithm (ICERA), Jun. 2025, doi: 10.1109/ICERA66156.2025.11087274.
  10. M. A. Al-Hashim, W. R. Fathel, H. D. Ali, and M. M. Al-Hatab, “Enhanced non-invasive blood glucose monitoring system employing wearable optical technology,” Frontiers in Photonics and Applications, vol. 19, no. 1, Jan. 2025, doi: 10.54216/FPA.190101.
  11. M. A. A. Mousa, A. Safwat, A. T. Elgohr, et al., "Attention-Enhanced CNNs and transformers for accurate monkeypox and skin disease detection," Dental Science Reports, 2025. Available: https://doi.org/10.1038/s41598-025-12216-y.
  12. J. Chen, Z. Lu, S. Kang, "Monkeypox disease recognition model based on improved SE-InceptionV3," Journal of Imaging and Functional Studies, 2024. Available: https://doi.org/10.3233/jifs-237232.
  13. S. Zi, "Monkeypox diagnosis with convolutional neural networks combined with colour space models," ACM Proceedings, 2022. Available: https://doi.org/10.1145/3584376.3584585.
  14. A.Ciran and E. Özbay, "Optimization-based feature selection in deep learning methods for monkeypox skin lesion detection," IEEE ISMSIT Proceedings, 2023. Available: https://doi.org/10.1109/ismsit58785.2023.10304930.
  15. D. A. Arafa, S. M. Ayyad, M. M. Abdelsalam, "MSCADMpox: A novel multi-stage classification model for effective monkeypox classification," Complex & Intelligent Systems, 2025. Available: https://doi.org/10.1007/s40747-025-02047-9.
  16. S. Savaş, "Enhancing disease classification with deep learning: A two-stage optimization approach for monkeypox and similar skin lesion diseases," Journal of Digital Imaging, vol. 37, pp. 1–14, 2024. Available: https://doi.org/10.1007/s10278-023-00941-7.
  17. V. Gokula Krishnan, B. S. Liya, S. V. Lakshmi, et al., "Monkeypox detection using hyper-parameter tuned based transferable CNN model," International Journal of Experimental Research and Review, vol. 33, SPL, 2023. Available: https://doi.org/10.52756/ijerr.2023.v33spl.003.
  18. K. Trang, A. H. Nguyen, N. G. Minh Thao, et al., "Performance enhancement in pre-trained deep learning models for monkeypox skin lesions identification using feature selection algorithms," SICE Proceedings, 2023. Available: https://doi.org/10.23919/sice59929.2023.10354166.
  19. A.A. Mishra, S. Mishra, A. Shastri, et al., "A hyper-tuned metrics based augmented CNN model for accurate prediction of monkeypox risk," IEEE ICEC Proceedings, 2024. Available: https://doi.org/10.1109/icec59683.2024.10837185.
  20. S. Islam, F. Nishi, T. Akter, et al., "Monkeypox skin lesion detection with deep learning and machine learning," International Journal of Computer Applications, 2023. Available: https://doi.org/10.5120/ijca2023922984.
  21. S. N. Ali, M. T. Ahmed, J. Paul, T. Jahan, S. M. S. Sakeef, N. Noor, and T. Hasan, "Monkeypox Skin Lesion Detection Using Deep Learning Models: A Preliminary Feasibility Study," arXiv preprint arXiv:2207.03342, 2022. [Online]. Available: https://arxiv.org/abs/2207.03342.
  22. T. Ojala, M. Pietikäinen, and D. Harwood, “A comparative study of texture measures with classification based on featured distributions,” Pattern Recognition, vol. 29, no. 1, pp. 51–59, 1996.
  23. R. C. Gonzalez and R. E. Woods, Digital Image Processing, 4th ed., Pearson, 2018.
  24. L. Xu, W. Liu, and Y. Liu, “Automated skin lesion analysis: A review,” Computers in Biology and Medicine, vol. 123, 103867, 2020.
  25. R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Trans. Systems, Man, and Cybernetics, vol. SMC-3, no. 6, pp. 610–621, 1973.
  26. Munirathinam, D. R., and M. Ranganadhan, "A new improved filter-based feature selection model for high-dimensional data," The Journal of Supercomputing, vol. 76, no. 8, pp. 5745–5762, 2020.
  27. M. S. Shang, L. Lü, W. Zeng, Y. C. Zhang, and T. Zhou, "Relevance is more significant than correlation: Information filtering on sparse data," Europhysics Letters, vol. 88, no. 6, p. 68008, 2010.
  28. P. T. Nguyen, J. Di Rocco, L. Iovino, D. Di Ruscio, and A. Pierantonio, "Evaluation of a machine learning classifier for metamodels," Software and Systems Modeling, vol. 20, no. 6, pp. 1797–1821, 2021.
  29. A.C. Lorena, L. F. Jacintho, M. F. Siqueira, R. De Giovanni, L. G. Lohmann, A. C. De Carvalho, and M. Yamamoto, "Comparing machine learning classifiers in potential distribution modelling," Expert Systems with Applications, vol. 38, no. 5, pp. 5268–5275, 2011.
  30. Ž. Vujović, "Classification model evaluation metrics," International Journal of Advanced Computer Science and Applications, vol. 12, no. 6, pp. 599–606, 2021.

Copyright @ 2026 IRJIET. All Right Reserved.

Licensed underCreative Commons Attribution 4.0 International License.