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使用基于深度学习网络的乳房超声波检测水牛乳房炎的新方法
Xinxin Zhang1,2, Yuan Li1,2, Yiping Zhang1,2
1National Center for International Research on Animal Genetics, Breeding and Reproduction (NCIRAGBR), Ministry of Science and Technology of the People's Republic of China, Huazhong Agricultural University, Wuhan 430070, China.
Animals : an open access journal from MDPI
|March 13, 2024
概括
开发了一种深度学习模型,用于使用超声波图像在水牛中早期检测乳腺炎. 该模型实现了高精度,为农场早期疾病检测提供了实用解决方案.
科学领域:
- 兽医医学 兽医医学 兽医医学
- 人工智能的人工智能
- 动物科学动物科学
背景情况:
- 乳腺炎严重影响全球乳制品生产,导致牛奶产量减少,质量问题和成本增加.
- 早期发现乳腺炎对于最大限度地减少经济损失和防止水牛群中广泛爆发至关重要.
- 深度学习为从医学成像中准确诊断疾病提供了先进的功能.
研究的目的:
- 开发和验证一个深度学习网络,用于检测水牛在四分之一水平的乳腺炎.
- 用超声波图像和不同的体细胞计数 (SCC) 值来评估模型的性能.
- 为发展中国家的小农提供可访问的诊断工具.
主要方法:
- 利用了271只水牛的3054张乳头超声波图像,分为训练,验证和测试集.
- 使用了EfficientNet_b3模型与卷积块注意模块 (CBAM) 结合使用.
- 使用PolyLoss函数解决了类不平衡,并使用2x10^5和4x10^5细胞/毫升的SCC值评估了性能.
主要成果:
- 深度学习模型实现了70.02%的准确度,77.93%的特异性,63.11%的灵敏度,AUC为0.77,SCC值为2x10^5细胞/毫升.
- 在SCC值为4x10^5细胞/毫升时,性能得到改善,精度为75.93%,特异性为80.23%,灵敏度为70.35%,AUC为0.83.
- 该模型在定义为SCC ≥4x10^5细胞/毫升时,证明了乳腺炎的优异分类.
结论:
- 成功建立了一个深度神经网络模型,用于在水牛中检测季度水平的乳腺炎.
- 开发的模型,特别是具有4x10^5细胞/毫升的SCC值,适用于农场乳腺炎诊断.
- 这项研究为水牛的乳腺炎诊断提供了宝贵的理论基础,特别是在资源有限的环境中.
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