Deep Learning Based Diagnosis of Lumpy Skin Disease

Lumpy Skin Disease (LSD) is a rapidly spreading transboundary disease in cattle, primarily caused by the Lumpy Skin Disease Virus (LSDV), which belongs to the Capripoxvirus genus of the Poxviridae family. The disease is characterized by fever, nodules on the skin, mucous membrane damage, enlarged lymph nodes, and significant decline in productivity including milk yield and fertility. Traditional diagnosis methods such as physical inspection and laboratory testing (e.g., PCR or ELISA) often prove insufficient for early detection and are not feasible in rural or resource-constrained areas due to cost, infrastructure, and time limitations. This project proposes a deep learning-based solution using Convolutional Neural Networks (CNNs) to detect LSD from digital images of cattle skin. A carefully curated image dataset containing Normal, Mild, and Severe cases of LSD-affected cattle was used for training the model. All images were labeled and preprocessed through resizing, normalization, and augmentation techniques to improve model generalization. The CNN model architecture was designed using TensorFlow and Keras, consisting of multiple convolutional, pooling, and dense layers optimized through experimentation. The training process included techniques such as dropout, data augmentation, and early stopping to avoid overfitting and improve robustness. After training, the model showed promising accuracy in distinguishing between the severity levels of the disease. To simplify usability for real-world applications, the classification output was mapped to a binary