LANet: A Lesion-Aware Global–Local Network for Multi-Class Cattle Lumpy Skin Disease Classification

Lumpy Skin Disease (LSD) threatens cattle health and productivity, imposing economic burdens on livestock production and highlighting the need for reliable image-based classification methods. Although deep learning has demonstrated strong potential for disease-related image classification, distinguishing localized and fine-grained visual patterns remains challenging. In this study, we propose LANet, a Lesion-Aware Global–Local Network for multi-class cattle LSD classification. The proposed framework employs a pretrained ResNet50 backbone followed by two complementary feature branches: a global branch based on adaptive average pooling and a local branch that learns a spatial attention mask to emphasize informative feature regions before adaptive max pooling. The resulting global and local representations are integrated through a feature interaction module and subsequently refined using adaptive feature gating before classification. Experiments on an 8012-image, five-class cattle disease dataset demonstrate that LANet achieves 98.93% accuracy, 97.67% macro-precision, 98.23% macro-F1, and 98.43% Cohen’s kappa against the dataset annotations. The model has 59.18 M parameters, requires 6.621 GFLOPs under standard inference, and achieves an inference latency of 0.533 ms per image with a throughput of 1875.30 images/s. These results demonstrate the effectiveness of LANet’s explicit global–local feature-learning and integration strategy, which combines holistic feature aggregation, spatially guided local feature extraction, feature interaction, and adaptive gating for multi-class classification of cattle disease images based on the available dataset annotations.