# Increasing Rosacea Awareness Among Population Using Deep Learning and Statistical Approaches

arXiv: https://arxiv.org/abs/2411.07074 · HTML: https://mlsanity.com/paper/2411.07074/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 11 Nov 2024.

## Builds on (most cited first, 4 of 4)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)

## Built on by (most cited first, 4 of 4)

- [Interpretable Automatic Rosacea Detection with Whitened Cosine Similarity](https://mlsanity.com/paper/2504.08073/) (2025)
- [Laparoscopic Image Desmoking Using the U-Net with New Loss Function and Integrated Differentiable Wiener Filter](https://mlsanity.com/paper/2505.21634/) (2025)
- [Patch-based Automatic Rosacea Detection Using the ResNet Deep Learning Framework](https://mlsanity.com/paper/2509.09841/) (2025)
- [Privacy-Preserving Automated Rosacea Detection Based on Medically Inspired Region of Interest Selection](https://mlsanity.com/paper/2509.09844/) (2025)

## Abstract (arXiv)

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.