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

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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Publication Details

Journal
PLoS ONE
Published
2024-08-05
DOI
https://doi.org/10.1371/journal.pone.0302862
Citations
29
Primary Topic
Poxvirus research and outbreaks
Type
article
Field-Weighted Citation Impact
8.51

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article

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

Tehseen Mazhar, Mohamed Tahar Ben Othman, Sheikh Muhammad Saqib, Yazeed Yasin Ghadi et al.
29 citations
PLoS ONE
Poxvirus research and outbreaks
8.51
article

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

Tehseen Mazhar, Mohamed Tahar Ben Othman, Sheikh Muhammad Saqib, Yazeed Yasin Ghadi, Muhammad Iqbal, Tariq Shahazad, Sulaiman Alamro
article en
29 citations

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.

PLoS ONEVol. 19(8)
Qassim University (SA), COMSATS University Islamabad (PK), Al Ain University (AE), Gomal University (PK), Virtual University of Pakistan (PK)
Qassim University
Openalex Percentile: Top 2%
Poxvirus research and outbreaks
8.51
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