Hybrid CNN-Haralick Framework for Foot and Mouth Disease Classification in Cattle

Abstract

Foot and Mouth Disease (FMD) poses a major challenge to livestock health, resulting in notable economic losses and threatening of food security. This study hereby leverages an Artificial intelligence (AI) technique by introducing a hybrid classification model that combines Convolutional Neural Network (CNN) for spatial feature extraction with texture analysis using Haralick features. Evaluated on a curated dataset of FMD-infected and healthy cattle images, the hybrid model demonstrated a notable improvement over other existing pure deep learning and CNN models, achieving an overall classification accuracy of 94%. Generally, the framework exhibited a balanced f1-score, precision and recall across all classes, addressing challenges such as overlapping patterns and class imbalance. By leveraging complementary spatial and texture-based features, the approach enhances diagnostic accuracy, offering a novel approach for FMD classification. This research underscores the value of hybrid models in advancing veterinary diagnostics and lays the groundwork for broader applications in livestock disease monitoring systems.