i-manager Publications

Image-Based Lumpy Skin Disease Diagnosis: A Comprehensive Review of Deep Learning Models

Sonali Zunke*, Shruti Puppalwar**, Abhay Bhagat***, Pranay Manusmare****, Harshal Gonnade*****, Tanay Kubde******

*-****** Department of Computer Science and Engineering, S. B. Jain Institute of Technology, Management and Research, Nagpur, Maharashtra, India.

Periodicity:September - December'2025
DOI : https://doi.org/10.26634/jls.4.3.22496

Abstract

Lumpy Skin Disease (LSD) is a viral infection that impacts cattle. This may result in financial setbacks in the dairy and livestock sectors. Timely identification of the illness is essential for improved treatment and for halting its transmission. Conventional diagnostic approaches, including clinical assessments and lab examinations, require considerable time and resources. Recent advancements in artificial intelligence, particularly in image processing through machine learning, offer efficient methods for automated LSD detection. This evaluation provides an examination of existing techniques, contrasting their advantages and disadvantages. Key obstacles in practical implementation are examined, and avenues for future studies are proposed to enhance the precision and effectiveness of LSD detection systems.

Keywords

Lumpy Skin Disease, Image Analysis, Machine Learning, Livestock Health Surveillance, Illness Identification.

How to Cite this Article?

Zunke, S., Puppalwar, S., Bhagat, A., Manusmare, P., Gonnade, H., and Kubde, T. (2025). Image-Based Lumpy Skin Disease Diagnosis: A Comprehensive Review of Deep Learning Models. i-manager’s Journal on Life Sciences, 4(3), 22-28. https://doi.org/10.26634/jls.4.3.22496

References

[1]. Abdullah, W., Tanwar, S., & Abouhawwash, M. (2025). Deep learning-based detection of lumpy skin disease in livestock using CNNs. Sustainable Machine Intelligence Journal, 11, 1–10.

[2]. Al-Salihi, K. (2014). Lumpy skin disease: Review of literature. Mirror of Research in Veterinary Sciences and Animals, 3(3), 6-23.

[3]. Anwar, A., Na-Lampang, K., Preyavichyapugdee, N., & Punyapornwithaya, V. (2022). Lumpy skin disease outbreaks in Africa, Europe, and Asia (2005–2022): multiple change point analysis and time series forecast. Viruses, 14(10), 2203.

[4]. Bianchini, J., Simons, X., Humblet, M. F., & Saegerman, C. (2023). Lumpy skin disease: a systematic review of mode of transmission, risk of emergence and risk entry pathway. Viruses, 15(8), 1622.

[5]. Genemo, M. (2023). Detecting high-risk area for lumpy skin disease in cattle using deep learning feature. Advances in Artificial Intelligence Research, 3(1), 27-35.

[6]. Girma, E. (2021). Identify Animal Lumpy Skin Disease Using Image Processing and Machine Learning (Doctoral dissertation, St. Mary's University).

[7]. Kumar, N., Sharma, S., & Tripathi, B. N. (2025). Pathogenicity and virulence of lumpy skin disease virus: A comprehensive update. Virulence, 16(1), 2495108.

[8]. Muhammad Saqib, S., Iqbal, M., Tahar Ben Othman, M., Shahazad, T., Yasin Ghadi, Y., Al-Amro, S., & Mazhar, T. (2024). Lumpy skin disease diagnosis in cattle: A deep learning approach optimized with RMSProp and MobileNetV2. PloS One, 19(8), e0302862.

[9]. Mujahid, M., Khurshaid, T., Safran, M., Alfarhood, S., & Ashraf, I. (2024). Prediction of lumpy skin disease virus using customized CBAM-DenseNet-attention model. BMC Infectious Diseases, 24(1), 1181.

[10]. Mulatu, E., & Feyisa, A. (2018). Review: Lumpy skin disease. Journal of Veterinary Science & Technology, 9(535), 1-8.

[11]. Porco, A., Chea, S., Sours, S., Nou, V., Groenenberg, M., Agger, C., Tum, S., Chhuon, V., Sorn, S., Hong, C., Davis, B., Davis, S., Ken, S., Olson, S. H., & Fine, A. E. (2023). Case report: Lumpy skin disease in an endangered wild banteng (Bos javanicus) and initiation of a vaccination campaign in domestic livestock in Cambodia. Frontiers in Veterinary Science, 10, 1228505.

[12]. Rony, M., Barai, D., & Hasan, Z. (2021). Cattle external disease classification using deep learning techniques. In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-7). IEEE.

[13]. Senthilkumar, C., C, S., Vadivu, G., & Neethirajan, S. (2024). Early detection of lumpy skin disease in cattle using deep learning—a comparative analysis of pretrained models. Veterinary Sciences, 11(10), 510.

[14]. Shivaanivarsha, N., Lakshmidevi, P. B., & Josy, J. T. (2022). A ConvNet based real-time detection and interpretation of bovine disorders. In 2022 International Conference on Communication, Computing and Internet of Things (IC3IoT) (pp. 1-6). IEEE.

[15]. Singh, P., Prakash, J., & Srivastava, J. (2023). Lumpy skin disease virus detection on animals through machine learning method. In 2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC) (pp. 481-486). IEEE.

[16]. Wangmo, K., Gurung, R. B., Choden, T., Letho, S., Pokhrel, N., Lungten, L., & Tenzin, T. (2024). Seroprevalence and risk factors associated with bovine tuberculosis in cattle in Eastern Bhutan. PLoS Neglected Tropical Diseases, 18(5), e0012223.

[17]. Zhai, S. L. (2021). The Belt and Road of Animal Diseases. Frontiers in Veterinary Science, 8, 795556.

Image-Based Lumpy Skin Disease Diagnosis: A Comprehensive Review of Deep Learning Models

Abstract

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