Pain assessment in horses using automatic facial expression recognition through deep learning-based modeling.
Pain assessment in horses using automatic facial expression recognition through deep learning-based modeling.
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DOI：
10.1371/journal.pone.0258672
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发表时间：
2021
期刊：
影响因子：
3.7
通讯作者：
Zanella AJ
中科院分区：
文献类型：
--
作者：
Lencioni GC;de Sousa RV;de Souza Sardinha EJ;Corrêa RR;Zanella AJ
本研究的目的是开发和评估一种机器视觉算法来评估马的疼痛程度，使用基于马鬼脸量表（HGS）的自动计算分类器，并通过机器学习方法进行训练。马鬼脸量表的使用依赖于人类观察者，而人类观察者在大多数情况下都无法长时间评估动物，并且必须经过良好的训练才能正确应用评估系统。此外，即使经过充分的训练，一个不知名的人出现在痛苦的动物附近也会导致行为改变，使评估变得更加复杂。作为一种可能的解决方案，自动视频成像系统将能够更准确、更实时地监测马的疼痛反应，从而允许对受影响的动物进行更早的诊断和更有效的治疗。本研究基于对7匹阉割马的面部表情的评估，这些面部表情是通过位于馈线站顶部的视频系统收集的，在手术阉割前两天和手术阉割后四天，每天在4个不同的时间点拍摄图像。应用标记过程建立疼痛面部图像数据库，并使用机器学习方法训练计算疼痛分类器。机器视觉算法是通过卷积神经网络（CNN）的训练开发的，在将疼痛分为三个级别：不存在、中度存在和明显存在时，总体准确率为75.8%。在两类（无疼痛和有疼痛）之间进行分类时，总体准确率达到88.3%。虽然为了在日常生活中使用该系统还有一些改进，但该模型看起来很有前途，能够通过从视频图像中收集的面部表情自动测量马的疼痛程度。
The aim of this study was to develop and evaluate a machine vision algorithm to assess the pain level in horses, using an automatic computational classifier based on the Horse Grimace Scale (HGS) and trained by machine learning method. The use of the Horse Grimace Scale is dependent on a human observer, who most of the time does not have availability to evaluate the animal for long periods and must also be well trained in order to apply the evaluation system correctly. In addition, even with adequate training, the presence of an unknown person near an animal in pain can result in behavioral changes, making the evaluation more complex. As a possible solution, the automatic video-imaging system will be able to monitor pain responses in horses more accurately and in real-time, and thus allow an earlier diagnosis and more efficient treatment for the affected animals. This study is based on assessment of facial expressions of 7 horses that underwent castration, collected through a video system positioned on the top of the feeder station, capturing images at 4 distinct timepoints daily for two days before and four days after surgical castration. A labeling process was applied to build a pain facial image database and machine learning methods were used to train the computational pain classifier. The machine vision algorithm was developed through the training of a Convolutional Neural Network (CNN) that resulted in an overall accuracy of 75.8% while classifying pain on three levels: not present, moderately present, and obviously present. While classifying between two categories (pain not present and pain present) the overall accuracy reached 88.3%. Although there are some improvements to be made in order to use the system in a daily routine, the model appears promising and capable of measuring pain on images of horses automatically through facial expressions, collected from video images.
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DOI：
10.3390/ani11061643
发表时间：
2021-06-01
期刊：
Animals : an open access journal from MDPI
影响因子：
--
作者：
Andersen PH;Broomé S;Rashid M;Lundblad J;Ask K;Li Z;Hernlund E;Rhodin M;Kjellström H
通讯作者：
Kjellström H
影响因子：
3.7
作者：
Rashid M;Silventoinen A;Gleerup KB;Andersen PH
通讯作者：
Andersen PH
影响因子：
5.7
作者：
Mathis, Mackenzie Weygandt;Mathis, Alexander
通讯作者：
Mathis, Alexander
影响因子：
3.7
作者：
Wathan, Jen;Burrows, Anne M.;McComb, Karen
通讯作者：
McComb, Karen
影响因子：
9.8
作者：
Flecknell, P.
通讯作者：
Flecknell, P.