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.

翻译：面部痤疮是一种常见疾病，尤其在青少年群体中多发，对患者的生理和心理均产生负面影响。对痤疮进行分类对于提供恰当的治疗至关重要。传统的目视检查或专家扫描耗时且难以区分痤疮类型。本文介绍了一种用于痤疮识别与分类的自动化专家系统。所提出的方法采用基于机器学习的技术，对六种类型的痤疮疾病进行分类与评估，以辅助皮肤科医生的诊断。预处理阶段包括对比度增强、平滑滤波以及RGB到L*a*b颜色空间的转换，以消除噪声并提高分类精度。随后，应用基于聚类的分割方法——k-means聚类，对通过特征提取步骤的病变区域进行分割。这些病变区域的特征基于灰度共生矩阵与统计特征的组合进行提取。最后，采用五种不同的机器学习分类器对痤疮疾病进行分类。实验结果表明，随机森林分类器取得了最高的准确率，达到98.50%，与现有先进方法相比具有显著优势。