论文标题
使用机器学习在面部特征上自动诊断外围和中央麻痹
Towards an Automatic Diagnosis of Peripheral and Central Palsy Using Machine Learning on Facial Features
论文作者
论文摘要
中央麻痹是一种面部麻痹的一种形式，需要紧急医疗护理，必须与其他类似的情况（例如外围性麻痹）区分开来。为了帮助快速准确诊断这种情况，我们提出了一种机器学习方法，以自动对外围和中央面部麻痹进行分类。使用了Palda数据集，其中包含103张外围性麻痹图像，40个中央麻痹和60位健康的人。实验是在五种机器学习算法上运行的。发现表现最好的算法是SVM（总准确度为85.1％）和高斯幼稚的贝叶斯（80.7％）。幼稚的贝叶斯方法实现了中央性麻痹的最低阴性率（80％，而70％）。这种情况可能被证明是最严重的，因此其敏感性是比较算法的另一种好方法。通过外推，估计数据集大小为334张图片，可达到95％的中心麻痹敏感性。这些机器学习实验的所有代码均可在https://github.com/cvvletter/palsy上在线免费获得。
Central palsy is a form of facial paralysis that requires urgent medical attention and has to be differentiated from other, similar conditions such as peripheral palsy. To aid in fast and accurate diagnosis of this condition, we propose a machine learning approach to automatically classify peripheral and central facial palsy. The Palda dataset is used, which contains 103 peripheral palsy images, 40 central palsy, and 60 healthy people. Experiments are run on five machine learning algorithms. The best performing algorithms were found to be the SVM (total accuracy of 85.1%) and the Gaussian naive Bayes (80.7%). The lowest false negative rate on central palsy was achieved by the naive Bayes approach (80% compared to 70%). This condition could prove to be the most severe, and thus its sensitivity is another good way to compare algorithms. By extrapolation, a dataset size of 334 total pictures is estimated to achieve a central palsy sensitivity of 95%. All code used for these machine learning experiments is freely available online at https://github.com/cvvletter/palsy.
