Lumpy Skin Disease (LSD), caused by the Capripoxvirus, is a highly contagious viral infection impacting cattle and other livestock, resulting in significant economic losses due to decreased productivity and mortality. Early detection is critical to mitigate its spread and reduce its impact on agriculture and dairy sectors. This study investigates the efficacy of deep learning methodologies for detecting LSD through X-ray images, offering a non-invasive diagnostic approach to identify internal lesions and abnormalities associated with the disease. We perform a comprehensive comparative analysis of various deep learning architectures, including Convolutional Neural Networks (CNNs), such as LeNet-5, AlexNet, and ResNet-50, alongside transfer learning models, like VGG16, InceptionV3, and DenseNet121, which leverage pre-trained weights from large datasets like ImageNet. Additionally, hybrid models integrating CNNs with Long Short-Term Memory (LSTM) networks and attention mechanisms are evaluated to enhance lesion localization and classification accuracy . The dataset comprises pre-processed X-ray images from veterinary sources, augmented to improve model generalization. Performance is assessed using metrics, such as accuracy, precision, recall, F1- score, and ROC-AUC, revealing that transfer learning and hybrid models outperform traditional CNNs, with DenseNet121 achieving up to 95.4% accuracy and attention-based hybrids reaching 96.2%.. This research highlights the potential of advanced deep learning techniques to revolutionize LSD diagnosis, addressing limitations of conventional methods like subjectivity and delays . By identifying research gaps, such as dataset diversity and model interpretability, this study provides a foundation for developing scalable, AI-driven diagnostic tools, contributing to improved livestock health management and sustainable agriculture.

Keywords: Lumpy skin disease, X-ray images, deep learning, convolutional neural networks, transfer learning, image classification, animal disease detection

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1. Deep learning for animal disease detection: A review. J Vet Sci. 2022. Available from: https://www.medrxiv.org.
2. Transfer learning for X-ray image classification: Applications in veterinary medicine. J Mach Learn Healthc. 2023.
3. Swarnkar SK, Guru A, Chhabra GS, Devarajan HR. Artificial intelligence revolutionizing cancer care – Precision diagnosis and patient-centric healthcare. Boca Raton: CRC Press; 2025.
4. Gururaj HL, Flammini F, Kumar VR, Prema NS. Recent trends in healthcare innovation. Boca Raton: CRC Press; 2025.
5. Soni HK, Sharma S, Sinha GR. Text and social media analytics for fake news and hate speech detection. Boca Raton: CRC Press; 2024.
6. Sharma VS, Mahajan S, Nayyar A, Pandit AK. Deep learning in engineering, energy and finance - Principles and applications. Boca Raton: CRC Press; 2024.
7. Chowa SS, Bhuiyan MRI, Payel IJ, Karim A, et al. A low complexity efficient deep learning model for automated retinal disease diagnosis. J Healthc Inform Res. 2025.
8. Convolutional neural networks for animal disease detection: A comparative study. Int J Comput Vis Image Process. 2021.
9. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60–88. doi: 10.1016/j.media.2017.07.005.
10. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–8. doi: 10.1038/nature21056.
11. Rajpurkar P, Irvin J, Ball RL, Zhu K, Yang B, Mehta H, et al. Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Med. 2018;15(11):e1002686. doi: 10.1371/journal.pmed.1002686.
12. Shen D, Wu G, Suk H-I. Deep learning in medical image analysis. Annu Rev Biomed Eng. 2017;19:221–48. doi: 10.1146/annurev-bioeng-071516-044442.
13. Ravi D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, et al. Deep learning for health informatics. IEEE J Biomed Health Inform. 2017;21(1):4–21. doi: 10.1109/JBHI.2016.2636665.

Lumpy Skin Disease (LSD), caused by the Capripoxvirus, is a highly contagious viral infection impacting cattle and other livestock, resulting in significant economic losses due to decreased productivity and mortality. Early detection is critical to mitigate its spread and reduce its impact on agriculture and dairy sectors. This study investigates the efficacy of deep learning methodologies for detecting LSD through X-ray images, offering a non-invasive diagnostic approach to identify internal lesions and abnormalities associated with the disease. We perform a comprehensive comparative analysis of various deep learning architectures, including Convolutional Neural Networks (CNNs), such as LeNet-5, AlexNet, and ResNet-50, alongside transfer learning models, like VGG16, InceptionV3, and DenseNet121, which leverage pre-trained weights from large datasets like ImageNet. Additionally, hybrid models integrating CNNs with Long Short-Term Memory (LSTM) networks and attention mechanisms are evaluated to enhance lesion localization and classification accuracy . The dataset comprises pre-processed X-ray images from veterinary sources, augmented to improve model generalization. Performance is assessed using metrics, such as accuracy, precision, recall, F1- score, and ROC-AUC, revealing that transfer learning and hybrid models outperform traditional CNNs, with DenseNet121 achieving up to 95.4% accuracy and attention-based hybrids reaching 96.2%.. This research highlights the potential of advanced deep learning techniques to revolutionize LSD diagnosis, addressing limitations of conventional methods like subjectivity and delays . By identifying research gaps, such as dataset diversity and model interpretability, this study provides a foundation for developing scalable, AI-driven diagnostic tools, contributing to improved livestock health management and sustainable agriculture.

Keywords: Lumpy skin disease, X-ray images, deep learning, convolutional neural networks, transfer learning, image classification, animal disease detection

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Deep learning for animal disease detection: A review. J Vet Sci. 2022. Available from: https://www.medrxiv.org.
2. Transfer learning for X-ray image classification: Applications in veterinary medicine. J Mach Learn Healthc. 2023.
3. Swarnkar SK, Guru A, Chhabra GS, Devarajan HR. Artificial intelligence revolutionizing cancer care – Precision diagnosis and patient-centric healthcare. Boca Raton: CRC Press; 2025.
4. Gururaj HL, Flammini F, Kumar VR, Prema NS. Recent trends in healthcare innovation. Boca Raton: CRC Press; 2025.
5. Soni HK, Sharma S, Sinha GR. Text and social media analytics for fake news and hate speech detection. Boca Raton: CRC Press; 2024.
6. Sharma VS, Mahajan S, Nayyar A, Pandit AK. Deep learning in engineering, energy and finance - Principles and applications. Boca Raton: CRC Press; 2024.
7. Chowa SS, Bhuiyan MRI, Payel IJ, Karim A, et al. A low complexity efficient deep learning model for automated retinal disease diagnosis. J Healthc Inform Res. 2025.
8. Convolutional neural networks for animal disease detection: A comparative study. Int J Comput Vis Image Process. 2021.
9. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60–88. doi: 10.1016/j.media.2017.07.005.
10. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–8. doi: 10.1038/nature21056.
11. Rajpurkar P, Irvin J, Ball RL, Zhu K, Yang B, Mehta H, et al. Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Med. 2018;15(11):e1002686. doi: 10.1371/journal.pmed.1002686.
12. Shen D, Wu G, Suk H-I. Deep learning in medical image analysis. Annu Rev Biomed Eng. 2017;19:221–48. doi: 10.1146/annurev-bioeng-071516-044442.
13. Ravi D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, et al. Deep learning for health informatics. IEEE J Biomed Health Inform. 2017;21(1):4–21. doi: 10.1109/JBHI.2016.2636665.