Noise-Robust Porcine Respiratory Diseases Classification Using Texture Analysis and CNN

KIPS Transactions on Software and Data Engineering, Vol. 7, No.3, pp.91-98, March 2018
10.3745/KTSDE.2018.7.3.091, Full Text

Abstract

Automatic detection of pig wasting diseases is an important issue in the management of group-housed pigs. In particular, porcine respiratory diseases are one of the main causes of mortality among pigs and loss of productivity in intensive pig farming. In this paper, we propose a noise-robust system for the early detection and recognition of pig wasting diseases using sound data. In this method, first we convert one-dimensional sound signals to two-dimensional gray-level images by normalization, and extract texture images by means of dominant neighborhood structure technique. Lastly, the texture features are then used as inputs of convolutional neural networks as an early anomaly detector and a respiratory disease classifier. Our experimental results show that this new method can be used to detect pig wasting diseases both economically (low-cost sound sensor) and accurately (over 96% accuracy) even under noise-environmental conditions, either as a standalone solution or to complement known methods to obtain a more accurate solution.


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Cite this paper

[KIPS Transactions Style]
Y. Choi, J. Lee, D. Park, and Y. Chung, "Noise-Robust Porcine Respiratory Diseases Classification Using Texture Analysis and CNN," KIPS Transactions on Software and Data Engineering, Vol.7, No.3, pp.91-98, 2018, DOI: 10.3745/KTSDE.2018.7.3.091.

[IEEE Style]
Yongju Choi, Jonguk Lee, Daihee Park, and Yongwha Chung, "Noise-Robust Porcine Respiratory Diseases Classification Using Texture Analysis and CNN," KIPS Transactions on Software and Data Engineering, vol. 7, no. 3, pp. 91-98, 2018. DOI: 10.3745/KTSDE.2018.7.3.091.

[ACM Style]
Choi, Y., Lee, J., Park, D., and Chung, Y. 2018. Noise-Robust Porcine Respiratory Diseases Classification Using Texture Analysis and CNN. KIPS Transactions on Software and Data Engineering, 7, 3, (2018), 91-98. DOI: 10.3745/KTSDE.2018.7.3.091.