# An Automatic Diagnosis Method of Facial Acne Vulgaris Based on Convolutional Neural Network

arXiv: https://arxiv.org/abs/1711.04481 · HTML: https://mlsanity.com/paper/1711.04481/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2017.

## Built on by (most cited first, 3 of 3)

- [ACNet: Mask-Aware Attention with Dynamic Context Enhancement for Robust Acne Detection](https://mlsanity.com/paper/2105.14891/) (2021)
- [Automatic Facial Skin Feature Detection for Everyone](https://mlsanity.com/paper/2203.16056/) (2022)
- [In-Depth Analysis of Automated Acne Disease Recognition and Classification](https://mlsanity.com/paper/2503.02835/) (2025)

## Abstract (arXiv)

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.