In-Depth Analysis of Automated Acne Disease Recognition and Classification

2025-03-04

AI Summary

This study addresses the time-consuming, subjective, and subtyping-challenging nature of manual clinical diagnosis of adolescent facial acne. We propose a highly interpretable and generalizable six-class fine-grained automatic recognition and grading system. Methodologically, we introduce the first systematic integration of L*a*b color space transformation, k-means–based adaptive lesion segmentation, GLCM-based texture features, and statistical features (mean, variance, entropy), combined with five classifiers—including random forest, which achieves 98.50% accuracy, significantly outperforming existing approaches. Our key contributions are: (1) a multimodal feature fusion framework tailored to real-world skin imagery; (2) co-optimization of lesion segmentation and clinically interpretable feature extraction; and (3) empirical validation of strong robustness and clinical applicability on unseen data.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

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.

Problem

• Automated acne recognition and classification system
• Machine learning for accurate acne type differentiation
• Improving dermatologist diagnosis with high-accuracy classification

Innovation

• Machine learning for acne classification
• K-means clustering for disease segmentation
• Random Forest achieves 98.50% accuracy