ArticlePloS one2024
Lumpy skin disease diagnosis in cattle: A deep learning approach optimized with RMSProp and MobileNetV2.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Classification of Foot-and-Mouth and Lumpy Skin Disease in Cattle Using Frozen Pretrained Encoders: A Comparison of Convolutional and Transformer Architectures.Veterinary sciences · 2026Article
- Robust multi-class lumpy skin disease diagnosis for practical livestock applications.Scientific reports · 2026Article
- Closed-loop artificial intelligence agents for animal epidemic prediction and decision support in livestock farming: a review.Frontiers in veterinary science · 2026Review
- Implementation of a Deep Learning System for Detection and Classification of Lumpy Skin Disease in Cattle: Enhancing Precision and Efficiency in Veterinary Diagnostics.Veterinary medicine and science · 2025Article
- Transfer Learning-Based Ensemble of CNNs and Vision Transformers for Accurate Melanoma Diagnosis and Image Retrieval.Diagnostics (Basel, Switzerland) · 2025Article
- An automatic approach for the classification of lumpy skin disease in cattle.Tropical animal health and production · 2025Article
- Automatic melanoma and non-melanoma skin cancer diagnosis using advanced adaptive fine-tuned convolution neural networks.Discover oncology · 2025Article
- An intelligent diagnostic method for porcine gastrointestinal infectious diseases based on multimodal AI and large language model.Frontiers in veterinary science · 2025Article
- Prediction of lumpy skin disease virus using customized CBAM-DenseNet-attention model.BMC infectious diseases · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
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