# In-Depth Analysis of Automated Acne Disease Recognition and Classification

arXiv: https://arxiv.org/abs/2503.02835 · HTML: https://mlsanity.com/paper/2503.02835/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 43 references that are in the map.
- First posted on arXiv 4 Mar 2025.

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

- [Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning](https://mlsanity.com/paper/1811.12808/) (2018)
- [An Automatic Diagnosis Method of Facial Acne Vulgaris Based on Convolutional Neural Network](https://mlsanity.com/paper/1711.04481/) (2017)
- [A Computer Vision Application for Assessing Facial Acne Severity from Selfie Images](https://mlsanity.com/paper/1907.07901/) (2019)
- [Decision Making with Machine Learning and ROC Curves](https://mlsanity.com/paper/1905.02810/) (2019)

## Abstract (arXiv)

Facial acne is a common disease, especially among adolescents, negatively affecting both physically and psychologically. Classifying acne is vital to providing the appropriate treatment. Traditional visual inspection or expert scanning is time-consuming and difficult to differentiate acne types. This paper introduces an automated expert system for acne recognition and classification. The proposed method employs a machine learning-based technique to classify and evaluate six types of acne diseases to facilitate the diagnosis of dermatologists. The pre-processing phase includes contrast improvement, smoothing filter, and RGB to L*a*b color conversion to eliminate noise and improve the classification accuracy. Then, a clustering-based segmentation method, k-means clustering, is applied for segmenting the disease-affected regions that pass through the feature extraction step. Characteristics of these disease-affected regions are extracted based on a combination of gray-level co-occurrence matrix (GLCM) and Statistical features. Finally, five different machine learning classifiers are employed to classify acne diseases. Experimental results show that the Random Forest (RF) achieves the highest accuracy of 98.50%, which is promising compared to the state-of-the-art methods.