Automated Foot and Mouth Disease Classification Using Transfer Learning-Based Deep Convolutional Neural Network

Foot and Mouth Disease is the most highly contagious disease around the world. Early detection of FMD is crucial for controlling this disease. Convolutional Neural Networks is the most convenient architecture in the state-of-the-art of Image Processing and Computer Vision sector. As per our knowledge, there was no method for FMD detection in the livestock sector which has been proposed by using CNN architecture. This proposed model aims that early detection of the highly infectious FMD using different types of CNN architectures with a pre-trained model like GoogleNet, AlexNet, and Residual Networks (ResNet). All the essential steps for executing the FMD with the Healthy cattle detection process are thoroughly explained in the paper, from the data assemble to the procedure and result. The proposed system is prominent to be successful, achieving results with 95% accuracy, which perhaps inhibit the human mistakes in the diagnosis process and would be conducive to recognize epidemic disease especially the FMD for veterinarians and cattle farmers.