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牛结节性皮肤病诊断:基于 RMSProp 和 MobileNetV2 优化的深度学习方法。

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

机构信息

Institute of Computing and Information Technology, Gomal University, Dera Ismail Khan, Pakistan.

Department of Computer Science, College of Computer, Qassim University, Buraydah, Saudi Arabia.

出版信息

PLoS One. 2024 Aug 5;19(8):e0302862. doi: 10.1371/journal.pone.0302862. eCollection 2024.

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.

摘要

牛结节疹病(Lumpy skin disease,简称 LSD)是牛群面临的一个重大问题,不仅影响个体奶牛,也会影响整个牛群。鉴于牛在满足人类需求方面的关键作用,有效管理这种疾病对于防止重大损失至关重要。本研究提出了一种使用 MobileNetV2 模型和 RMSprop 优化器的深度学习方法来应对这一挑战。在一组健康和患有牛结节疹病的牛的图像数据集上进行的测试显示,其准确率高达 95%,比现有基准提高了 4-10%。这些结果突显了所提出的方法在牛结节疹病诊断和管理方面的潜力,有助于推动奶牛养殖业的革新。我们的论文面向研究人员和研究生。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c29f/11299804/4f71ce7864eb/pone.0302862.g001.jpg

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