Robust multi-class lumpy skin disease diagnosis for practical livestock applications

Abstract

Lumpy skin disease (LSD) is a highly contagious viral infection that severely impacts cattle health and livestock productivity worldwide, necessitating reliable and automated diagnostic solutions. Recent advances in deep learning (DL) have demonstrated significant potential for image-based disease classification; however, achieving robust performance under real-world variability and class imbalance remains a critical challenge. In this study, we propose ConvNeXtLSD, a DL-based multi-class classification framework for automated cattle LSD diagnosis. The proposed model is built upon a modified ConvNeXt backbone architecture, consisting of a patchify stem followed by four hierarchical feature extraction stages composed of ConvNeXt blocks and downsampling layers, enabling effective multi-scale representation learning. To enhance class separability, the standard classification head is redesigned using feature flattening and layer normalization, which improves feature distribution stability and discriminative capability. Extensive experiments on a five-class cattle LSD dataset containing 8,014 images demonstrate that ConvNeXtLSD achieves superior classification performance, with an accuracy of 98.29%, a precision of 95.82%, an F1-score of 96.58%, and a Cohen’s Kappa coefficient of 97.51%. Moreover, the proposed framework maintains practical computational efficiency with 15.372 GFLOPs and a real-time inference speed of 90.18 FPS, demonstrating an effective balance between predictive performance and computational cost for real-world livestock disease diagnosis.