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一种自动的方法来对牛群的块性皮肤病进行分类
Fakhre Alam1, Asad Ullah2, Mohammed A Rohaim3,4
1Department of Computer Science and Information Technology, University of Malakand, Dir, Pakistan. fakhrealam@uom.edu.pk.
Tropical animal health and production
|May 28, 2025
概括
本研究引入了一种自动机器学习算法,用于在牛群中对皮病 (LSD) 的分类. 支持向量机模型实现了84%的准确性,改善了牲畜的疾病管理.
科学领域:
- 兽医医学 兽医医学 兽医医学
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 块状皮肤病 (LSD) 对全球畜牧业构成重大经济威胁.
- 手动诊断LSD是劳动密集型,耗时,需要专家知识.
- 自动化分类系统对于有效和准确的疾病检测至关重要.
研究的目的:
- 开发和评估一种自动机器学习算法,用于分类牛群的块性皮肤病.
- 为了比较支持矢量机器 (SVM) 与其他机器学习模型的性能,用于LSD分类.
主要方法:
- 利用一个精心策划的图像数据集从健康和感染LSD的牛.
- 雇佣Inception V3用于从牛病变模式中提取特征.
- 应用支持向量机 (SVM) 用于图像分类.
主要成果:
- SVM模型的整体准确率达到了84%.
- 该模型的精度为80%,回忆率为83%,F1得分为82%.
- SVM的表现优于物流回归,随机森林,决策树和AdaBoost模型.
结论:
- 拟议的自动化算法有效地分类了牛中的块性皮肤疾病.
- 与其他测试模型相比,SVM在LSD分类方面表现优越.
- 未来的工作包括数据集的扩展和探索先进的算法,如视觉转换器,以提高准确性.
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