Clinical Image Classification of Cattle Foot and Mouth Disease Based on Convolutional Neural Network

Abstract

Foot-and-Mouth Disease (FMD) is a highly contagious viral outbreak affecting cattle and causes significant economic losses to the national livestock industry. The limited availability of veterinary experts in the field often leads to delayed diagnosis, which contributes to the rapid spread of the virus. This study aims to develop an intelligent computational model capable of automatically diagnosing clinical symptoms of FMD from digital images using a deep learning approach based on Convolutional Neural Networks (CNNs). The research methodology begins with the collection of a dataset consisting of images of cattle mouths and hooves, categorized into two classes: FMD-infected and healthy. The preprocessing stage involves image resizing and pixel normalization, followed by data augmentation techniques such as rotation and flipping to reduce overfitting. The model architecture is designed using a sequence of convolutional layers, pooling layers, and fully connected layers to automatically extract visual features related to lesion characteristics. Based on the experimental results, the proposed model achieves high classification performance, with a validation accuracy of 95%. The dataset used in this study consists of 1,000 image samples, with a data split ratio of 70% for training, 15% for validation, and 15% for testing. In addition to accuracy, the classification performance demonstrates a recall of 96%, F1-score of 94%, and precision of 91%.The findings of this study confirm that a computer vision–based approach can serve as a reliable tool for early diagnostic assistance, offering fast and accurate detection to support better decision-making in livestock health management.

References

Kementerian Pertanian Republik Indonesia, Pedoman Siaga Darurat Veteriner Indonesia: Penyakit Mulut dan Kuku (Kiatvetindo-PMK). Jakarta: Direktorat Jenderal Peternakan dan Kesehatan Hewan, 2022.

C. Stenfeldt, J. M. Pacheco, L. L. Rodriguez, and J. Arzt, “Infection dynamics of foot-and-mouth disease virus in pigs using two novel simulated-natural inoculation methods,” Research in Veterinary Science, vol. 104, pp. 96–105, 2016.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.

S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 10, pp. 1345–1359, 2010.

R. C. Gonzalez and R. E. Woods, Digital Image Processing, 4th ed. New York, NY, USA: Pearson, 2018.

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.

A. Kamilaris and F. Prenafeta-Boldú, “Deep learning in agriculture: A survey,” Computers and Electronics in Agriculture, vol. 147, pp. 70–90, 2018.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in Proceedings of the 3rd International Conference on Learning Representations (ICLR), San Diego, CA, USA, 2015.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” Journal of Machine Learning Research, vol. 15, no. 1, pp. 1929–1958, 2014.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 770–778.

M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. C. Chen, “MobileNetV2: Inverted residuals and linear bottlenecks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA, 2018, pp. 4510–4520.

J. Arzt, B. Baxt, M. J. Grubman, and T. Jackson, “The pathogenesis of foot-and-mouth disease II: Viral pathways in swine, small ruminants, and wildlife; myotropism, chronic syndromes, and molecular virus-host interactions,” Transboundary and Emerging Diseases, vol. 58, no. 4, pp. 305–326, 2011.

S. Sharma, S. Kumar, and A. Tiwari, “Deep learning applications for disease diagnosis in livestock: A review,” Computers and Electronics in Agriculture, vol. 176, p. 105637, 2020.

F. Chollet, Deep Learning with Python. Shelter Island, NY, USA: Manning Publications, 2017.

D. P. King, N. J. Knowles, and J. Wadsworth, “Genomic sequencing and foot-and-mouth disease virus lineages,” in Foot and Mouth Disease Virus: Current Research and Emerging Trends, F. Sobrino and E. Domingo, Eds. Poole, UK: Caister Academic Press, 2017, pp. 15–30.

X. Li, L. Zhang, and Z. Wu, “Deep learning in precision agriculture: A review,” International Journal of Agricultural and Biological Engineering, vol. 14, no. 3, pp. 1–10, 2021.

M. Abadi et al., “TensorFlow: A system for large-scale machine learning,” in Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI), Savannah, GA, USA, 2016, pp. 265–283.

N. A. I. Supardi, R. Rosnelly, and T. W. Purboyo, “Cattle skin disease classification using Convolutional Neural Network,” Journal of Physics: Conference Series, vol. 1844, p. 012009, 2021.

C. Shorten and T. M. Khoshgoftaar, “A survey on image data augmentation for deep learning,” Journal of Big Data, vol. 6, no. 1, p. 60, 2019.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in Proceedings of the International Conference on Learning Representations (ICLR), San Diego, CA, USA, 2015.

G. W. Smith, “Foot and Mouth Disease: Clinical signs and diagnosis,” Veterinary Clinics of North America: Food Animal Practice, vol. 35, no. 2, pp. 245–256, 2019.

Issue

Vol. 11 No. 1 (2026): JUSTINDO

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Copyright (c) 2026 Muammar Reza Pahlawan Reza, Sahriani, Shabarul Mukjizat

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