A Comparative Analysis of Deep Learning Models
for Detection of Lumpy Skin Disease with emphasis
on Shifted Window Transformers

A Comparative Analysis of Deep Learning Models
for Detection of Lumpy Skin Disease with emphasis
on Shifted Window Transformers

Deep learning, Lumpy Skin Disease (LSD), Vision
Transformer, CNN, livestock diagnosis, image analysis, model
explainability, dataset imbalance, veterinary AI

Lumpy Skin Disease (LSD) in cattle is an increasingly
prevalent viral infection with significant economic impact.
Traditional detection methods are often labor-intensive and
delayed. In this study, five state-of-the-art deep learning (DL)
architectures—ResNet50, EfficientNetB0, MobileNetV2, Vision
Transformer (ViT-B16), and Swin Transformer Tiny (Swin-T)—
were evaluated and compared for image-based LSD classification.
Publicly available Kaggle datasets of infected and healthy cattle
were used. All models were fine-tuned using transfer learning and
tested for classification accuracy, F1-score, inference time,
explainability (via Grad-CAM), and real-world deployability.
Results show that Swin-T achieved the highest classification
accuracy of 95.3%, while MobileNetV2 emerged as the most
deployment-friendly model. Grad-CAM visualizations confirmed
that transformer-based models captured relevant lesion features
with greater spatial sensitivity than CNNs. The study highlights
the promise of hybrid transformer-CNN models for practical
livestock diagnostics, especially in resource-constrained
environments

George Mwangi Muhindi

https://ijcit.com/index.php/ijcit/article/view/533

Department of Information Technology
Murang’a University of Technology
Murang’a, Kenya.

september 2025

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