A Deep Learning Tool for Early Detection and Control of Lumpy Skin Disease Using Convolutional Neural Networks

Abstract

Lumpy skin disease (LSD), a highly contagious viral disease of cattle, continues to pose a significant threat to animal welfare and global economic stability. Early detection and intervention are crucial for mitigating its impact. This research explored the potential of convolutional neural networks (CNNs) for automated LSD classification based on clinical and laboratory data. We compared two prominent CNN architectures, Inception and Xception, in their ability to identify patterns and predict LSD occurrence. Both models were trained on a large dataset of labeled images, effectively learning to distinguish LSD-infected animals from healthy ones. However, Xception emerged as the superior technique, achieving a remarkable 98.8% accuracy compared to Inception's 94%. This 4.8% improvement in accuracy demonstrates the potential of Xception for more precise and reliable LSD detection. These findings suggest that CNNs, particularly Xception, can be valuable tools for early LSD diagnosis, enabling prompt veterinary intervention and reducing disease spread. Integrating this technology into veterinary practices can significantly improve animal health management and disease control efforts, ultimately minimizing LSD's global impact on cattle populations.