دورية أكاديمية
Lumpy skin disease diagnosis in cattle: A deep learning approach optimized with RMSProp and MobileNetV2.
العنوان: | Lumpy skin disease diagnosis in cattle: A deep learning approach optimized with RMSProp and MobileNetV2. |
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المؤلفون: | Muhammad Saqib S; Institute of Computing and Information Technology, Gomal University, Dera Ismail Khan, Pakistan., Iqbal M; Institute of Computing and Information Technology, Gomal University, Dera Ismail Khan, Pakistan., Tahar Ben Othman M; Department of Computer Science, College of Computer, Qassim University, Buraydah, Saudi Arabia., Shahazad T; Department of Computer Science, COMSATS University Islamabad, Sahiwal, Pakistan., Yasin Ghadi Y; Department of Computer Science and Software Engineering, Al Ain University, Abu Dhabi, United Arab Emirates., Al-Amro S; Department of Computer Science, College of Computer, Qassim University, Buraydah, Saudi Arabia., Mazhar T; Department of Computer Science, Virtual University of Pakistan, Lahore, Pakistan. |
المصدر: | PloS one [PLoS One] 2024 Aug 05; Vol. 19 (8), pp. e0302862. Date of Electronic Publication: 2024 Aug 05 (Print Publication: 2024). |
نوع المنشور: | Journal Article |
اللغة: | English |
بيانات الدورية: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
أسماء مطبوعة: | Original Publication: San Francisco, CA : Public Library of Science |
مواضيع طبية MeSH: | Deep Learning* , Lumpy Skin Disease*/diagnosis, Cattle ; Animals |
مستخلص: | 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. Competing Interests: NO. (Copyright: © 2024 Muhammad Saqib et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.) |
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تواريخ الأحداث: | Date Created: 20240805 Date Completed: 20240805 Latest Revision: 20240807 |
رمز التحديث: | 20240807 |
مُعرف محوري في PubMed: | PMC11299804 |
DOI: | 10.1371/journal.pone.0302862 |
PMID: | 39102387 |
قاعدة البيانات: | MEDLINE |
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