Hybrid CNN-Haralick Framework for Foot and Mouth Disease Classification in Cattle
Oluwaseyi Ezekiel Olorunshola
Department of Computer Science, Faculty of Computing, Air Force Institute of Technology, Kaduna, Nigeria.
Nanji Emmanuella Lakan *
Department of Computer Science, Faculty of Computing, Air Force Institute of Technology, Kaduna, Nigeria.
Fatimah Adamu-Fika
Department of Cyber Security, Faculty of Computing, Air Force Institute of Technology, Kaduna, Nigeria.
Adeniran Kolade Ademuwagun
Department of Cyber Security, Faculty of Computing, Air Force Institute of Technology, Kaduna, Nigeria.
*Author to whom correspondence should be addressed.
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.
Keywords: Machine learning, convolutional neural network, Haralick features, hybrid classification framework, foot and mouth disease, livestock health