Machine Learning Based Diagnosis of Lumpy Skin Disease

Abstract

Lumpy Skin Disease (LSD) is a severe viral condition that affects cattle, posing a serious risk to the global livestock sector. Containing its spread requires prompt and precise diagnosis, as delays can lead to substantial economic damage. Conventional diagnostic techniques—such as physical examination and laboratory analysis—can be slow and may not always yield highly sensitive results. In light of these challenges, machine learning (ML) has gained attention as a promising solution for enhancing disease detection, offering both speed and accuracy. This study introduces an innovative diagnostic model for identifying Lumpy Skin Disease in cattle by leveraging machine learning algorithms. A diverse dataset was compiled, consisting of clinical symptoms and histopathological findings from infected animals, alongside data from healthy cattle for comparative analysis. Prior to model training, the data underwent thorough preprocessing, including noise removal and normalization, to ensure consistency and quality. The proposed ML-based system offers two key benefits. First, it enables rapid and non-invasive detection of LSD, significantly reducing the time needed to identify outbreaks. This allows for quicker intervention and more effective containment of the virus. Second, the system demonstrates high accuracy in distinguishing between healthy and infected animals, minimizing the chances of diagnostic errors and reducing financial losses associated with misdiagnosis. Overall, this approach has the potential to transform the detection and management of LSD. By incorporating intelligent algorithms into veterinary diagnostics, the system supports proactive disease control, boosts cattle health, and strengthens the resilience of the livestock industry against viral threats like LSD.