An Automatic Diagnosis Method of Facial Acne Vulgaris Based on Convolutional Neural Network
This work addresses the problem of improving classification accuracy for facial acne diagnosis in dermatology, but it is incremental as it builds on existing CNN methods with a focus on specific classifiers.
The paper tackles automatic diagnosis of facial acne vulgaris by proposing a method using convolutional neural networks (CNNs) to classify skin areas and acne types, with results showing that a pre-trained VGG16 network outperforms their custom CNN in feature extraction and achieves good robustness in skin detection and acne classification.
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.