An automatic approach for the classification of lumpy skin disease in cattle

Lumpy Skin Disease (LSD) presents significant risks and economic challenges to global cattle farming. Effective and accurate classification of LSD is essential for managing the disease and reducing its impacts. Manual diagnosis is time-consuming, labor-intensive, and requires experienced personnel. Automated classification methods provide advantages by reducing labor and improving accuracy. This study proposes an automated algorithm for LSD classification using machine learning. The method uses a carefully curated dataset of images from both LSD-infected cattle and healthy cattle. Inception V3 was employed to extract features from complex lesion patterns in infected cattle images, comparing them to healthy cattle images. Support Vector Machines (SVM) were used to classify the extracted features. The results show the model achieved an 84% accuracy rate, with precision at 80%, recall at 83%, and an F1 score of 82%. These results were compared with other machine learning models, including Logistic Regression, Random Forest, Decision Tree, and AdaBoost. SVM outperformed other models, demonstrating consistent evaluation precision at 0.84. For further enhancement, expanding the dataset with high-quality images and applying advanced machine learning algorithms like Vision Transformers (ViTs), MobileNetV2, and Visual Geometry Group (VGG) could refine automated LSD classification. The aim is to improve disease management practices in the livestock industry through better classification systems.