Lumpy skin disease diagnosis in cattle: A deep learning approach optimized with RMSProp and MobileNetV2

• Published in: PloS one 2024-08, Vol.19 (8), p.e0302862
• Main Authors: Muhammad Saqib, Sheikh, Iqbal, Muhammad, Tahar Ben Othman, Mohamed, Shahazad, Tariq, Yasin Ghadi, Yazeed, Al-Amro, Sulaiman, Mazhar, Tehseen
• Format: Article
• Language: English
• Subjects: Accuracy; Agricultural production; Analysis; Animal populations; Animals; Automation; Beef cattle; Benchmarks; Biology and Life Sciences; Cataracts; Cattle; Classification; Computer and Information Sciences; Deep Learning; Diagnosis; Disease management; Feature selection; Glaucoma; International economic relations; Livestock farming; Livestock industry; Lumpy skin disease; Lumpy Skin Disease - diagnosis; Machine learning; Medical diagnosis; Medical research; Medicine and Health Sciences; Medicine, Experimental; Methods; Neural networks; Research and Analysis Methods; Skin; Skin cancer; Skin diseases; Skin tests; Trade restrictions
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0302862

Lumpy skin disease (LSD) is a critical problem for cattle populations, affecting both individual cows and the entire herd. Given cattle's critical role in meeting human needs, effective management of this disease is essential to prevent significant losses. The study proposes a deep learning approach using the MobileNetV2 model and the RMSprop optimizer to address this challenge. Tests on a dataset of healthy and lumpy cattle images show an impressive accuracy of 95%, outperforming existing benchmarks by 4-10%. These results underline the potential of the proposed methodology to revolutionize the diagnosis and management of skin diseases in cattle farming. Researchers and graduate students are the audience for our paper.