A Robust Ensemble Deep Learning Model for Lumpy Skin Disease Identification
Received: 16 September 2025 | Revised: 27 October 2025 | Accepted: 12 November 2025 | Online: 9 February 2026
Corresponding author: Bain Khusnul Khotimah
Abstract
Early diagnosis of Lumpy Skin Disease (LSD) in cattle is crucial for maintaining meat quality and ensuring livestock productivity. This study introduces an innovative Ensemble Deep Learning (EDL) framework with Contrast Limited Adaptive Histogram Equalization (CLAHE) preprocessing, L2 regularization-based feature optimization, and adaptive ensemble weighting to efficiently and accurately detect LSD. Comparative experiments were conducted with feature selection methods, such as Information Gain (IG), Chi-square, and L1 regularization, as well as ensemble methods, such as Simple Averaging, Majority Voting, and Stacking. The L2-norm-based proposed ensemble attained the highest Dice score of 97.50%, the highest Jaccard index of 98.20%, and the best AUC of 0.980 at α = 0.01. It also attained an accuracy of 99.70%, precision of 98.73%, recall of 96.20%, and a 97.80% F1-score, with a moderate Inference Time (IT) of 0.17 s per image. In comparison to marginally slower but simpler ensemble methods, it also exhibited large improvements in robustness, generalization, and feature stability. Overall, when combined, the ensemble optimization and feature regularization enable the Regularized-ensemble Convolutional Neural Network (RoFR-eCNN) to deliver precise, accurate LSD detection, providing a scalable and efficient framework for automatic and early livestock disease diagnosis.
Keywords:
LSD, CLAHE, L2 regularization, CNN architectures, ensemble deep learning, feature selection, bioinformatics, food security, weight ensembleDownloads
References
A. Haider et al., "The Economic Impact of Lumpy Skin Disease and Cost-Effectiveness of Vaccination for the Control of Outbreaks in Pakistan," Journal of Veterinary Medicine and Animal Sciences, vol. 6, no. 1, May 2023.
E. S. M. Tuppurainen et al., "Review: Capripoxvirus Diseases: Current Status and Opportunities for Control," Transboundary and Emerging Diseases, vol. 64, no. 3, pp. 729–745, June 2017. DOI: https://doi.org/10.1111/tbed.12444
G. K. Hussien, M. H. Khafagy, and H. M. Elbehiery, "The Effect of Climate Change on Animal Diseases by Using Image Processing and Deep Learning Techniques," International Journal of Advanced Computer Science and Applications, vol. 16, no. 3, 2025. DOI: https://doi.org/10.14569/IJACSA.2025.0160360
Y. Song et al., "Emergence of Lumpy Skin Disease Virus Infection in Yaks, Cattle‐Yaks, and Cattle on the Qinghai–Xizang Plateau of China," Transboundary and Emerging Diseases, vol. 2024, no. 1, Jan. 2024, Art. no. 2383886. DOI: https://doi.org/10.1155/2024/2383886
G. Mallikarjun and V. A. Narayana, "Harnessing Adapted Capsule Networks for Accurate Lumpy Skin Disease Diagnosis in Cattle," IAES International Journal of Artificial Intelligence, vol. 13, no. 4, Dec. 2024, Art. no. 3909. DOI: https://doi.org/10.11591/ijai.v13.i4.pp3909-3919
F. Alam, A. Ullah, M. A. Rohaim, M. Munir, and A. Hussain, "An Automatic Approach for the Classification of Lumpy Skin Disease in Cattle," Tropical Animal Health and Production, vol. 57, no. 5, June 2025, Art. no. 230. DOI: https://doi.org/10.1007/s11250-025-04475-8
C. Senthilkumar, C. Sindhu, G. Vadivu, and S. Neethirajan, "Early Detection of Lumpy Skin Disease in Cattle Using Deep Learning—A Comparative Analysis of Pretrained Models," Veterinary Sciences, vol. 11, no. 10, Oct. 2024, Art. no. 510. DOI: https://doi.org/10.3390/vetsci11100510
A. A. AlZubi, "Lumpy Skin Disease Detection in Cattle by a Robust Approach using Advanced Convolutional Neural Networks," Indian Journal of Animal Research, vol. 58, no. 12, pp. 2146–2153, Dec. 2024. DOI: https://doi.org/10.18805/IJAR.BF-1793
D. K. Saha, "An Extensive Investigation of Convolutional Neural Network Designs for the Diagnosis of Lumpy Skin Disease in Dairy Cows," Heliyon, vol. 10, no. 14, Jul. 2024, Art. no. e34242. DOI: https://doi.org/10.1016/j.heliyon.2024.e34242
M. Z. Shakeel et al., "A Deep Learning Tool for Early Detection and Control of Lumpy Skin Disease Using Convolutional Neural Networks," Journal of Computing & Biomedical Informatics, vol. 7, no. 2, 2024.
S. Muhammad Saqib et al., "Lumpy Skin Disease Diagnosis in Cattle: a Deep Learning Approach Optimized with RMSProp and MobileNetV2," PLOS ONE, vol. 19, no. 8, Aug. 2024, Art. no. e0302862. DOI: https://doi.org/10.1371/journal.pone.0302862
M. Mujahid, T. Khurshaid, M. Safran, S. Alfarhood, and I. Ashraf, "Prediction of Lumpy Skin Disease Virus using Customized CBAM-Densenet-Attention Model," BMC Infectious Diseases, vol. 24, no. 1, Oct. 2024, Art. no. 1181. DOI: https://doi.org/10.1186/s12879-024-10032-9
O. M. Olaniyan, O. J. Adetunji, and A. M. Fasanya, "Development of a Model for the Prediction of Lumpy Skin Diseases using Machine Learning Techniques," ABUAD Journal of Engineering Research and Development, vol. 6, no. 2, pp. 100–112, Oct. 2023. DOI: https://doi.org/10.53982/ajerd.2023.0602.10-j
R. Kumar and S. Soni, "Classification of Lumpy Skin Disease using Principal Component Analysis (PCA)-based Supervised Machine Learning," International Journal of Trend in Scientific Research and Development, vol. 8, no. 3, pp. 648–653, June 2024.
A. Kaur and K. Singh, "Evaluating Machine Learning Methods Voting System for Predicting the Occurrence of Lumpy Skin Condition," SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology, vol. 15, no. 03, pp. 326–330, Nov. 2023. DOI: https://doi.org/10.18090/samriddhi.v15i03.07
S. A. Khaskheli, M. Y. Koondhar, Z. A. Maher, G. B. Khaskheli, and A. A. Khaskheli, "A Model for Early Detection of Lumpy Skin Disease in Cattle Using Ensemble Technique," Pakistan Journal of Zoology, vol. 57, no. 4, 2025. DOI: https://doi.org/10.17582/journal.pjz/20230810102300
A. Mohammed and R. Kora, "A Comprehensive Review on Ensemble Deep Learning: Opportunities and Challenges," Journal of King Saud University - Computer and Information Sciences, vol. 35, no. 2, pp. 757–774, Feb. 2023. DOI: https://doi.org/10.1016/j.jksuci.2023.01.014
S. D. Taufiqoh and P. D. Purnamasari, "Development of Detection Model For Skin Diseases In Pets Using Image Processing And Deep Learning Techniques," International Journal of Electrical, Computer, and Biomedical Engineering, vol. 3, no. 2, pp. 437–457, June 2025. DOI: https://doi.org/10.62146/ijecbe.v3i2.114
E. A. Safavi, "Lumpy Skin Disease Dataset." Mendeley, [Online]. Available: https://data.mendeley.com/datasets/7pyhbzb2n9/1.
S. Phimphisan and N. Sriwiboon, "A Customized CNN Architecture with CLAHE for Multi-Stage Diabetic Retinopathy Classification," Engineering, Technology & Applied Science Research, vol. 14, no. 6, pp. 18258–18263, Dec. 2024. DOI: https://doi.org/10.48084/etasr.8932
H. K. Dishar and L. A. Muhammed, "A Review of the Overfitting Problem in Convolution Neural Network and Remedy Approaches," Journal of Al-Qadisiyah for Computer Science and Mathematics, vol. 15, no. 2, pp. 155–165, Sept. 2023. DOI: https://doi.org/10.29304/jqcm.2023.15.2.1240
A. Rezaeezade and L. Batina, "Regularizers to the Rescue: Fighting Overfitting in Deep Learning-based Side-channel Analysis," Journal of Cryptographic Engineering, vol. 14, no. 4, pp. 609–629, Nov. 2024. DOI: https://doi.org/10.1007/s13389-024-00361-5
S. J. Pan and Q. Yang, "A Survey on Transfer Learning," IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 10, pp. 1345–1359, Oct. 2010. DOI: https://doi.org/10.1109/TKDE.2009.191
F. Zhuang et al., "A Comprehensive Survey on Transfer Learning," Proceedings of the IEEE, vol. 109, no. 1, pp. 43–76, Jan. 2021. DOI: https://doi.org/10.1109/JPROC.2020.3004555
M. Lin, Q. Chen, and S. Yan, "Network In Network," 2nd International Conference on Learning Representations, Banff, Canada, Dec. 2013.
D. Kumar Saha et al., "Mpox-XDE: An Ensemble Model Utilizing Deep CNN and Explainable AI for Monkeypox Detection and Classification," BMC Infectious Diseases, vol. 25, no. 1, Mar. 2025, Art. no. 403. DOI: https://doi.org/10.1186/s12879-025-10811-y
L. Breiman, "Bagging Predictors," Machine Learning, vol. 24, no. 2, pp. 123–140, Aug. 1996. DOI: https://doi.org/10.1023/A:1018054314350
T. G. Dietterich, "Ensemble Methods in Machine Learning," in Multiple Classifier Systems, vol. 1857, Berlin, Heidelberg, Germany: Springer Berlin Heidelberg, 2000, pp. 1–15. DOI: https://doi.org/10.1007/3-540-45014-9_1
T. Mazi, R. Bollepally, S. T. K. Reddy, and N. Chandu, "A Deep Learning Method to Identify Lumpy Skin Disease in Cows," International Research Journal of Modernization in Engineering Technology and Science, vol. 5, no. 6, pp. 4163–4170, June 2023.
E. S. Nugroho, I. Ardiyanto, and H. A. Nugroho, "Enhancing Dermoscopic Pigmented Skin Lesion Classification: A Refined Approach Using the Pre-trained Inception-V3 Architecture," Narra J, vol. 5, no. 2, Apr. 2025, Art. no. e1852. DOI: https://doi.org/10.52225/narra.v5i2.1852
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, "MobileNetV2: Inverted Residuals and Linear Bottlenecks," in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, June 2018, pp. 4510–4520. DOI: https://doi.org/10.1109/CVPR.2018.00474
A. Andri, "Deep Learning Incorporated with Augmented Reality Application for Watch Try-On," Journal of Applied Data Sciences, vol. 6, no. 1, pp. 259–271, Jan. 2024. DOI: https://doi.org/10.47738/jads.v6i1.529
Y. Harjoseputro, Ign. P. Yuda, and K. P. Danukusumo, "MobileNets: Efficient Convolutional Neural Network for Identification of Protected Birds," International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 6, pp. 2290–2296, Dec. 2020. DOI: https://doi.org/10.18517/ijaseit.10.6.10948
P. Paygude, S. Kumar, R. Singh, M. Saxena, and P. Chavan, "Comparative Analysis of CNN vs Vision Transformer for Lumpy Skin Disease in Cattle," Indian Society for Technical Education, vol. 46, pp. 487–493, July 2023.
Downloads
How to Cite
License
Copyright (c) 2025 Bain Khusnul Khotimah, Budi Dwi Satoto, Yoga Dwitya Pramudita, Deshinta Arrova Dewi, Firdatul A’yuni
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
