Automated Diagnosis of Acne and Rosacea using Convolution Neural Networks
Firas Gerges, Frank Y. Shih, Danielle Azar
Abstract
Acne and Rosacea are two common skin diseases that affect many people worldwide. These two skin conditions can result in similar signs, which leads to the misdiagnosis of the case. People affected by these two skin rashes, usually tend not to seek medical diagnosis from expert dermatologists, but instead rely on over-the-counter medications and beauty products for self-treatment. Although acne and rosacea are both usually considered non-dangerous, treating acne with rosacea medication (and vice-versa) can lead to worsen symptoms. In this paper, we propose a deep learning model that can automatically distinguish Rosacea from Acne cases using infected skin images. Due to the limited number of available images, we enlarged the data set using image augmentation. Experimental results show that our model achieves a high performance with an average testing accuracy of 87.1% (over 10-folds) and 91.2% on the validation set. The good predictive performance of the model depicts its usability to classify new, unseen cases. We believe that such a model can serve as an efficient basis to build an automatic acne-rosacea distinguishing software tool.