  In this paper, we present a new automatic diagnosis method of facial acne
vulgaris based on convolutional neural network. This method is proposed to
overcome the shortcoming of classification types in previous methods. The core
of our method is to extract features of images based on convolutional neural
network and achieve classification by classifier. We design a binary classifier
of skin-and-non-skin to detect skin area and a seven-classifier to achieve the
classification of facial acne vulgaris and healthy skin. In the experiment, we
compared the effectiveness of our convolutional neural network and the
pre-trained VGG16 neural network on the ImageNet dataset. And we use the ROC
curve and normal confusion matrix to evaluate the performance of the binary
classifier and the seven-classifier. The results of our experiment show that
the pre-trained VGG16 neural network is more effective in extracting image
features. The classifiers based on the pre-trained VGG16 neural network achieve
the skin detection and acne classification and have good robustness.
