Comparison Analysis of Lumpy Skin Disease Detection System Using X-Ray Images of Animals with the Help of Deep Learning

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Volume: 11 | Issue: 2 | Year 2025 | Subscription
International Journal of Animal Biotechnology and Applications
Received Date: 04/25/2024
Acceptance Date: 09/15/2025
Published On: 2025-10-04
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By: Ranveer Singh

Assistant Professor, Faculty of Computing, Guru Kashi University, Talwandi Sabo, Punjab, India.

Abstract

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 [1]. Early detection is critical to mitigate its spread and reduce its impact on agriculture and dairy sectors [2]. 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 [3]. 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 [4, 5]. Additionally, hybrid models integrating CNNs with Long Short-Term Memory (LSTM) networks and attention mechanisms are evaluated to enhance lesion localization and classification accuracy [6]. 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% [7]. This research highlights the potential of advanced deep learning techniques to revolutionize LSD diagnosis, addressing limitations of conventional methods like subjectivity and delays [8]. 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 [9, 10].

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

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Citation:

How to cite this article: Ranveer Singh, Comparison Analysis of Lumpy Skin Disease Detection System Using X-Ray Images of Animals with the Help of Deep Learning. International Journal of Animal Biotechnology and Applications. 2025; 11(2): -p.

How to cite this URL: Ranveer Singh, Comparison Analysis of Lumpy Skin Disease Detection System Using X-Ray Images of Animals with the Help of Deep Learning. International Journal of Animal Biotechnology and Applications. 2025; 11(2): -p. Available from:https://journalspub.com/publication/ijaba/article=21095

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