Increasing Rosacea Awareness Among Population Using Deep Learning and Statistical Approaches

TL;DR

The proposed automatic rosacea detection methods are able to automatically distinguish patients who are suffering from rosacea from people who are clean of this disease and address the explainability issue that allows doctors and patients to understand and trust the results.

Abstract

Approximately 16 million Americans suffer from rosacea according to the National Rosacea Society. To increase rosacea awareness, automatic rosacea detection methods using deep learning and explainable statistical approaches are presented in this paper. The deep learning method applies the ResNet-18 for rosacea detection, and the statistical approaches utilize the means of the two classes, namely, the rosacea class vs. the normal class, and the principal component analysis to extract features from the facial images for automatic rosacea detection. The contributions of the proposed methods are three-fold. First, the proposed methods are able to automatically distinguish patients who are suffering from rosacea from people who are clean of this disease. Second, the statistical approaches address the explainability issue that allows doctors and patients to understand and trust the results. And finally, the proposed methods will not only help increase rosacea awareness in the general population but also help remind the patients who suffer from this disease of possible early treatment since rosacea is more treatable at its early stages. The code and data are available at https://github.com/chengyuyang-njit/rosacea_detection.git.

Figures (2)

• Figure 1: The first column shows the mean images of the two datasets from the rosacea negative and positive classes, respectively. The remaining five columns display five random example images from the two datasets corresponding to the normal people and those with rosacea, respectively.
• Figure 2: The training/validation loss and accuracy of the ResNet-18.