Nielen M, Schukken Y H, Hogeveen H
Department of Herd Health and Reproduction, College of Veterinary Medicine, Utrecht, The Netherlands.
Vet Res. 1994;25(2-3):285-9.
Some aspects of automated clinical mastitis detection and diagnosis are discussed. Knowledge representation techniques for the different steps in the diagnostic process are presented. The main focus of this paper is on automated early detection, based on data that are automatically collected in the milking parlour. Principal component analysis, logistic regression and back-propagation neural networks were used in the analysis of the automatically collected data. The 3 techniques did not differ greatly in performance. All the techniques performed better when data from milking with observed clinical signs were used, compared with data from milking before clinical symptoms were noticed. Healthy quarters were mostly correctly classified by all techniques. It seems unlikely that all clinical mastitis cases can be detected at milking before visible clinical signs occur.
本文讨论了自动化临床乳腺炎检测与诊断的一些方面。介绍了诊断过程中不同步骤的知识表示技术。本文的主要重点是基于在挤奶厅自动收集的数据进行自动化早期检测。主成分分析、逻辑回归和反向传播神经网络被用于分析自动收集的数据。这三种技术在性能上差异不大。与临床症状出现前挤奶的数据相比，使用有观察到临床症状的挤奶数据时，所有技术的表现都更好。所有技术大多能正确分类健康的乳腺区。似乎不太可能在挤奶时在可见临床症状出现之前检测到所有临床乳腺炎病例。
