Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, HangZhou, 310027, China.
Sci Rep. 2018 Apr 11;8(1):5839. doi: 10.1038/s41598-018-24204-6.
In this paper, we present a new automatic diagnosis method for facial acne vulgaris which is based on convolutional neural networks (CNNs). To overcome the shortcomings of previous methods which were the inability to classify enough types of acne vulgaris. The core of our method is to extract features of images based on CNNs and achieve classification by classifier. A binary-classifier of skin-and-non-skin is used to detect skin area and a seven-classifier is used to achieve the classification task of facial acne vulgaris and healthy skin. In the experiments, we compare the effectiveness of our CNN and the VGG16 neural network which is pre-trained on the ImageNet data set. We use a ROC curve to evaluate the performance of binary-classifier and use a normalized confusion matrix to evaluate the performance of seven-classifier. The results of our experiments show that the pre-trained VGG16 neural network is effective in extracting features from facial acne vulgaris images. And the features are very useful for the follow-up classifiers. Finally, we try applying the classifiers both based on the pre-trained VGG16 neural network to assist doctors in facial acne vulgaris diagnosis.
在本文中，我们提出了一种基于卷积神经网络（CNN）的新的面部寻常痤疮自动诊断方法。为了克服以前的方法的缺点，这些方法无法对足够类型的寻常痤疮进行分类。我们方法的核心是基于 CNN 提取图像的特征，并通过分类器实现分类。使用二分类器（皮肤和非皮肤）来检测皮肤区域，并用七分类器来实现面部寻常痤疮和健康皮肤的分类任务。在实验中，我们比较了我们的 CNN 和预训练在 ImageNet 数据集上的 VGG16 神经网络的有效性。我们使用 ROC 曲线来评估二分类器的性能，并使用归一化混淆矩阵来评估七分类器的性能。实验结果表明，预训练的 VGG16 神经网络在从面部寻常痤疮图像中提取特征方面非常有效，并且这些特征对于后续的分类器非常有用。最后，我们尝试将基于预训练的 VGG16 神经网络的分类器应用于辅助医生进行面部寻常痤疮诊断。
