دورية أكاديمية
أعرض في EDS

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

التفاصيل البيبلوغرافية
العنوان: Lumpy skin disease diagnosis in cattle: A deep learning approach optimized with RMSProp and MobileNetV2.
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.)
References: Viruses. 2023 Jul 25;15(8):. (PMID: 37631965)
IEEE Trans Biomed Eng. 2016 Nov;63(11):2326-2335. (PMID: 26886962)
Viruses. 2023 Aug 31;15(9):. (PMID: 37766268)
Vaccines (Basel). 2023 Mar 02;11(3):. (PMID: 36992162)
Biomed Opt Express. 2019 Jan 25;10(2):892-913. (PMID: 30800522)
Comput Methods Programs Biomed. 2018 Aug;162:221-231. (PMID: 29903489)
Br J Biomed Sci. 2010;67(4):177-83. (PMID: 21294444)
Trop Anim Health Prod. 2014 Jan;46(1):241-6. (PMID: 24097247)
J Med Syst. 2019 Jul 4;43(8):269. (PMID: 31273532)
Front Neurosci. 2019 May 17;13:422. (PMID: 31156359)
Healthcare (Basel). 2023 Feb 01;11(3):. (PMID: 36766989)
Sci Rep. 2021 Nov 16;11(1):22292. (PMID: 34785733)
IEEE Trans Fuzzy Syst. 2021 Jan;29(1):34-45. (PMID: 33408453)
Comput Med Imaging Graph. 2011 Mar;35(2):99-104. (PMID: 21035303)
J Virol. 2022 Aug 10;96(15):e0075122. (PMID: 35867566)
Conf Proc IEEE Eng Med Biol Soc. 2006;2006:4775-8. (PMID: 17946649)
PeerJ Comput Sci. 2023 Dec 5;9:e1651. (PMID: 38192457)
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
الوصف
تدمد:1932-6203
DOI:10.1371/journal.pone.0302862