A patient’s face usually suffers from multiple types of acne lesions at the same time. The data sets and the classifier in this article are all designed to detect these six types of acne vulgaris (blackheads, whiteheads, papules, pustules, modules and cysts) mentioned above on human faces. In this article, we adopt a type of classification consisting of non-inflammatory lesions (open comedones called blackheads and closed comedones called whiteheads), inflammatory lesions (papules and pustules) and relatively more severe types, nodules and cysts 1. Because of its diversity, we cannot find a unified classification method to assist in diagnosis.
Finally, we try applying the classifiers both based on the pre-trained VGG16 neural network to assist doctors in facial acne vulgaris diagnosis.Īcne is a chronic skin disease whose characteristics and mechanisms are very complex. And the features are very useful for the follow-up classifiers. The results of our experiments show that the pre-trained VGG16 neural network is effective in extracting features from facial acne vulgaris images. 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. In the experiments, we compare the effectiveness of our CNN and the VGG16 neural network which is pre-trained on the ImageNet data set. 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.
The core of our method is to extract features of images based on CNNs and achieve classification by classifier. To overcome the shortcomings of previous methods which were the inability to classify enough types of acne vulgaris. In this paper, we present a new automatic diagnosis method for facial acne vulgaris which is based on convolutional neural networks (CNNs).
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