Deep Learning Based Image Analysis for Classification of Foot and Mouth Disease in Cattle

Foot and mouth disease (FMD) poses a substantial threat to the global cattle industry, as it is a highly contagious viral infection. Timely detection and precise classification of FMD in cattle are essential for effective disease management and control. The proposed method harnesses the power of convolutional neural networks (CNNs) to automatically extract distinguishing features from cattle images and classify whether the cattle is diseased or not. Furthermore, a pre-trained architecture such as MobileNetV2 is employed and fine-tuned for FMD classification. Experiments were performed to assess the effectiveness of the proposed approach, taking into account metrics such as accuracy, precision, recall, and F1-score. The experimental results demonstrate the efficacy of proposed deep learning-based classification approach, achieving impressive accuracy rates in differentiating between healthy and FMD-infected cattle.