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Automated Monkeypox Disease Classification Using Texture and Focus-Based Image Features

Tuğba Şentürk1,
Çiğdem Gülüzar Altıntop2,
Fatma Latifoğlu3✉
1Inonu University
2Erciyes University, Faculty of Engineering, Department of Biomedical Engineering, Kayseri, Türkiye
3Erciyes University
Received:Nov 5, 2025→Revised:Mar 12, 2026→Accepted:Mar 30, 2026→Published:March 30, 2026
DOI: 10.56038/ejrnd2026300790
Vol. 6, No. 1 · ejrnd2026300790

Abstract

Accurate diagnosis of monkeypox is challenging because it is a viral disease that causes skin lesions resembling other dermatological conditions. This study aims to present a machine learning-based approach for the classification of monkeypox skin lesions using image-based feature extraction techniques. In this study, texture-based features were extracted from skin lesion images using the Gray-Level Co-Occurrence Matrix (GLCM), Gray-Level Run Length Matrix (GLRLM), Local Binary Patterns (LBP), and Focus Measures for image sharpness analysis to improve classification performance. To address class imbalance and enhance model reliability, the Synthetic Minority Oversampling Technique (SMOTE) was applied. Four different classifiers Random Forest (RF), K-Nearest Neighbors (KNN), Naive Bayes (NB) and Decision Tree (DT) were evaluated using accuracy, precision, recall, specificity, F1-score and area under the curve (AUC) metrics. The results demonstrated that the highest classification performance was achieved by the Random Forest classifier, with an accuracy of 90.02% and an AUC of 96.10%, followed by the KNN classifier. Among the extracted features, LBP and Focus Measures provided the most discriminative information for monkeypox lesion classification. Overall, feature extraction and data balancing significantly improved classification performance. The findings indicate that machine learning-based classification combined with texture feature extraction and data balancing is effective for detecting monkeypox skin lesions. RF and KNN emerged as the most suitable classifiers among those tested. To the best of our knowledge, this is the first study to investigate monkeypox lesion classification using the specified extracted features. These results highlight the potential of image-based machine learning methods in supporting early diagnosis, while future studies should focus on larger datasets and hybrid deep learning approaches to further improve accuracy and generalizability.
Keywords
Monkeypoxmachine learningskin lesion classificationSMOTEfeature extraction

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References

  1. 1.[1] A. M. McCollum and I. K. Damon, “Human monkeypox,” Clin. Infect. Dis., vol. 58, no. 2, pp. 260–267, 2014.
  2. 2.[2] L. D. Bethineedi, L. V. S. Kutikuppala, and V. Kandi, “Monkeypox epidemic: a throwback from smallpox eradication,” Cureus, vol. 14, no. 7, 2022.
  3. 3.[3] B. Moss, “Poxvirus DNA replication,” Cold Spring Harb. Perspect. Biol., vol. 5, no. 9, p. a010199, 2013.
  4. 4.[4] M. Pal, F. Mengstie, and V. Kandi, “Epidemiology, diagnosis, and control of monkeypox disease: a comprehensive review,” Am J Infect Dis Microbiol, vol. 5, no. 2, pp. 94–99, 2017.
  5. 5.[5] A. K. Rao, “Use of JYNNEOS (smallpox and monkeypox vaccine, live, nonreplicating) for preexposure vaccination of persons at risk for occupational exposure to orthopoxviruses: recommendations of the Advisory Committee on Immunization Practices—United States, 2022,” MMWR. Morb. Mortal. Wkly. Rep., vol. 71, 2022.
  6. 6.[6] A. W. Rimoin et al., “Major increase in human monkeypox incidence 30 years after smallpox vaccination campaigns cease in the Democratic Republic of Congo,” Proc. Natl. Acad. Sci., vol. 107, no. 37, pp. 16262–16267, 2010.
  7. 7.[7] P. von Magnus, E. K. Andersen, K. B. Petersen, and A. Birch Andersen, “A pox-like disease in cynomolgus monkeys.,” 1959.
  8. 8.[8] J. G. Breman, M. V Steniowski, E. Zanotto, A. I. Gromyko, and I. Arita, “Human monkeypox, 1970-79,” Bull. World Health Organ., vol. 58, no. 2, p. 165, 1980.
  9. 9.[9] J. P. Thornhill et al., “Monkeypox virus infection in humans across 16 countries—April–June 2022,” N. Engl. J. Med., vol. 387, no. 8, pp. 679–691, 2022.
  10. 10.[10] W. H. Organization, “Monkeypox https://www. who. int/news-room/fact-sheets/detail/monkeypox,” Accessed 10th Jun, 2022.
  11. 11.[11] F. B. Demir et al., “MNPDenseNet: automated monkeypox detection using multiple nested patch division and pretrained densenet201,” Multimed. Tools Appl., pp. 1–23, 2024.
  12. 12.[12] D. Uzun Ozsahin, M. T. Mustapha, B. Uzun, B. Duwa, and I. Ozsahin, “Computer-aided detection and classification of monkeypox and chickenpox lesion in human subjects using deep learning framework,” Diagnostics, vol. 13, no. 2, p. 292, 2023.
  13. 13.[13] O. C. Abanobi, “Factors associated with the practice of monkey pox preventive behaviours among health workers in Yenagoa LGA, Bayelsa state, Nigeria”.
  14. 14.[14] N. Fatima and K. Mandava, “Monkeypox-a menacing challenge or an endemic?,” Ann. Med. Surg., vol. 79, p. 103979, 2022.
  15. 15.[15] A. S. Jaradat et al., “Automated monkeypox skin lesion detection using deep learning and transfer learning techniques,” Int. J. Environ. Res. Public Health, vol. 20, no. 5, p. 4422, 2023.
  16. 16.[16] M. M. Ahsan, M. R. Uddin, M. Farjana, A. N. Sakib, K. Al Momin, and S. A. Luna, “Image Data collection and implementation of deep learning-based model in detecting Monkeypox disease using modified VGG16,” arXiv Prepr. arXiv2206.01862, 2022.
  17. 17.[17] M. Manjurul Ahsan, M. Ramiz Uddin, and S. Akter Luna, “Monkeypox Image Data collection,” arXiv e-prints, p. arXiv-2206, 2022.
  18. 18.[18] C. Sitaula and T. B. Shahi, “Monkeypox virus detection using pre-trained deep learning-based approaches,” J. Med. Syst., vol. 46, no. 11, p. 78, 2022.
  19. 19.[19] S. N. Ali et al., “Monkeypox skin lesion detection using deep learning models: A feasibility study,” arXiv Prepr. arXiv2207.03342, 2022.
  20. 20.[20] M. A. Hussain, T. Islam, F. U. H. Chowdhury, and B. M. R. Islam, “Can artificial intelligence detect Monkeypox from digital skin images?,” BioRxiv, pp. 2008–2022, 2022.
  21. 21.[21] V. H. Sahin, I. Oztel, and G. Yolcu Oztel, “Human monkeypox classification from skin lesion images with deep pre-trained network using mobile application,” J. Med. Syst., vol. 46, no. 11, p. 79, 2022.
  22. 22.[22] D. J. Matuszewski and I.-M. Sintorn, “TEM virus images: Benchmark dataset and deep learning classification,” Comput. Methods Programs Biomed., vol. 209, p. 106318, 2021.
  23. 23.[23] A. A. Abdelhamid et al., “Classification of monkeypox images based on transfer learning and the Al-Biruni Earth Radius Optimization algorithm,” Mathematics, vol. 10, no. 19, p. 3614, 2022.
  24. 24.[24] U. Singh and L. S. Songare, “Analysis and detection of monkeypox using the GoogLeNet Model,” in 2022 International Conference on Automation, Computing and Renewable Systems (ICACRS), IEEE, 2022, pp. 1000–1008.
  25. 25.[25] M. Dwivedi, R. G. Tiwari, and N. Ujjwal, “Deep learning methods for early detection of monkeypox skin lesion,” in 2022 8th International Conference on Signal Processing and Communication (ICSC), IEEE, 2022, pp. 343–348.
  26. 26.[26] V. Kumar, “Analysis of CNN features with multiple machine learning classifiers in diagnosis of monkeypox from digital skin images,” MedRxiv, pp. 2009–2022, 2022.
  27. 27.[27] A. I. Saleh and A. H. Rabie, “Human monkeypox diagnose (HMD) strategy based on data mining and artificial intelligence techniques,” Comput. Biol. Med., vol. 152, p. 106383, 2023.
  28. 28.[28] S. Bengesi, T. Oladunni, R. Olusegun, and H. Audu, “A machine learning-sentiment analysis on monkeypox outbreak: An extensive dataset to show the polarity of public opinion from Twitter tweets,” IEEE Access, vol. 11, pp. 11811–11826, 2023.
  29. 29.[29] D. Bala et al., “MonkeyNet: A robust deep convolutional neural network for monkeypox disease detection and classification,” Neural Networks, vol. 161, pp. 757–775, 2023.
  30. 30.[30] F. Yasmin et al., “PoxNet22: A fine-tuned model for the classification of monkeypox disease using transfer learning,” Ieee Access, vol. 11, pp. 24053–24076, 2023.
  31. 31.[31] N. V Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: synthetic minority over-sampling technique,” J. Artif. Intell. Res., vol. 16, pp. 321–357, 2002.
  32. 32.[32] M. Yavaş, A. Güran, and M. Uysal, “Covid-19 veri kümesinin SMOTE tabanlı örnekleme yöntemi uygulanarak sınıflandırılması,” Avrupa Bilim ve Teknol. Derg., pp. 258–264, 2020.
  33. 33.[33] R. HEPHZIBAH, A. H. CHRISTINAL, D. M. ALEX, R. JAYANTHI, D. A. CHANDY, and C. BAJAJ, “A hybrid model of random forest ensemble and resample for cardiotocography data classification.,” Sigma J. Eng. Nat. Sci. ve Fen Bilim. Derg., vol. 43, no. 2, 2025.
  34. 34.[34] T. Subramaniam, H. A. Jalab, R. W. Ibrahim, and N. F. Mohd Noor, “Improved image splicing forgery detection by combination of conformable focus measures and focus measure operators applied on obtained redundant discrete wavelet transform coefficients,” Symmetry (Basel)., vol. 11, no. 11, p. 1392, 2019.
  35. 35.[35] S. Pertuz, D. Puig, and M. A. Garcia, “Analysis of focus measure operators for shape-from-focus,” Pattern Recognit., vol. 46, no. 5, pp. 1415–1432, 2013.
  36. 36.[36] R. M. Haralick, K. Shanmugam, and I. H. Dinstein, “Textural features for image classification,” IEEE Trans. Syst. Man. Cybern., no. 6, pp. 610–621, 1973.
  37. 37.[37] A. Çalışkan, E. Acar, and Y. Kaya, “GLCM TABANLI K-NN SINIFLANDIRICI MODELİ İLE AVUÇ İÇİ TANIMA SİSTEMİ,” Batman Üniversitesi Yaşam Bilim. Derg., vol. 1, no. 2, pp. 1–10, 2012.
  38. 38.[38] K. Gajalakshmi and S. Saravanan, “Automatic identification of brittle, elongated and equiaxed ductile fracture modes in weld joints through machine learning.,” Sigma J. Eng. Nat. Sci. ve Fen Bilim. Derg., vol. 43, no. 2, 2025.
  39. 39.[39] S. S. Sastry, T. V. Kumari, C. N. Rao, K. Mallika, S. Lakshminarayana, and H. S. Tiong, “Transition temperatures of thermotropic liquid crystals from the local binary gray level cooccurrence matrix,” Adv. Condens. Matter Phys., vol. 2012, no. 1, p. 527065, 2012.
  40. 40.[40] Ş. Öztürk and B. Akdemir, “Application of feature extraction and classification methods for histopathological image using GLCM, LBP, LBGLCM, GLRLM and SFTA,” Procedia Comput. Sci., vol. 132, pp. 40–46, 2018.
  41. 41.[41] F. Albregtsen, B. Nielsen, and H. E. Danielsen, “Adaptive gray level run length features from class distance matrices,” in Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, IEEE, 2000, pp. 738–741.
  42. 42.[42] A. K. Mohanty, S. Beberta, and S. K. Lenka, “Classifying benign and malignant mass using GLCM and GLRLM based texture features from mammogram,” Int. J. Eng. Res. Appl., vol. 1, no. 3, pp. 687–693, 2011.
  43. 43.[43] V. Durgamahanthi, J. Anita Christaline, and A. Shirly Edward, “GLCM and GLRLM based texture analysis: application to brain cancer diagnosis using histopathology images,” in Intelligent Computing and Applications: Proceedings of ICICA 2019, Springer, 2021, pp. 691–706.
  44. 44.[44] A. Gunay and V. V Nabiyev, “Automatic age classification with LBP,” in 2008 23rd international symposium on computer and information sciences, IEEE, 2008, pp. 1–4.
  45. 45.[45] M. Heikkilä, M. Pietikäinen, and C. Schmid, “Description of interest regions with local binary patterns,” Pattern Recognit., vol. 42, no. 3, pp. 425–436, 2009.
  46. 46.[46] T. Şentürk et al., “Histopathological Image Analysis Using Machine Learning to Evaluate Cisplatin and Exosome Effects on Ovarian Tissue in Cancer Patients,” Appl. Sci., vol. 15, no. 4, p. 1984, 2025.
  47. 47.[47] R. Muthukrishnan and R. Rohini, “LASSO: A feature selection technique in predictive modeling for machine learning,” in 2016 IEEE international conference on advances in computer applications (ICACA), Ieee, 2016, pp. 18–20.
  48. 48.[48] M. Suyal and P. Goyal, “A review on analysis of K-nearest neighbor classification machine learning algorithms based on supervised learning,” Int. J. Eng. Trends Technol., vol. 70, no. 7, pp. 43–48, 2022.
  49. 49.[49] H. Demir, P. Erdoğmuş, and M. Kekeçoğlu, “Destek Vektör Makineleri, YSA, K-Means ve KNN Kullanarak Arı Türlerinin Sınıflandırılması,” Düzce Üniversitesi Bilim ve Teknol. Derg., vol. 6, no. 1, pp. 47–67, 2018.
  50. 50.[50] A. Nafees et al., “Forecasting the mechanical properties of plastic concrete employing experimental data using machine learning algorithms: DT, MLPNN, SVM, and RF,” Polymers (Basel)., vol. 14, no. 8, p. 1583, 2022.
  51. 51.[51] P. Dinesh, A. S. Vickram, and P. Kalyanasundaram, “Medical image prediction for diagnosis of breast cancer disease comparing the machine learning algorithms: SVM, KNN, logistic regression, random forest and decision tree to measure accuracy,” in AIP Conference Proceedings, AIP Publishing, 2024.
  52. 52.[52] B. Daş and İ. Türkoğlu, “DNA dizilimlerinin sınıflandırılmasında karar ağacı algoritmalarının karşılaştırılması,” 2014.
  53. 53.[53] H. T. Zaw, N. Maneerat, and K. Y. Win, “Brain tumor detection based on Naïve Bayes Classification,” in 2019 5th International Conference on engineering, applied sciences and technology (ICEAST), IEEE, 2019, pp. 1–4.
  54. 54.[54] T. Özcan, “Yığınlanmış özdevinimli kodlayıcılar ile göğüs kanserinin sınıflandırılması ve klasik makine öğrenme metotları ile performans karşılaştırması,” Erciyes Üniversitesi Fen Bilim. Enstitüsü Fen Bilim. Derg., vol. 36, no. 2, pp. 151–160, 2020.

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Şentürk, T., Altıntop, Ç. G., Latifoğlu, F. (2026). Automated Monkeypox Disease Classification Using Texture and Focus-Based Image Features . *The European Journal of Research and Development*, 6(1), ejrnd2026300790. https://doi.org/10.56038/ejrnd2026300790

Bibliographic Info

JournalThe European Journal of Research and Development
Volume6
Issue1
Pages1–16
Article IDejrnd2026300790
PublishedMarch 30, 2026
eISSN2822-2296