用机器学习自动识别六类痤疮，准确率达98.5%。
In-Depth Analysis of Automated Acne Disease Recognition and Classification
- 通过颜色转换与滤波预处理提升图像质量
- 结合GLCM和统计特征提取病灶区域特征
- 随机森林分类器效果最优，适合临床辅助诊断
面部痤疮是青少年常见疾病，对身心均有负面影响。准确分类对治疗至关重要，但传统人工判读耗时且难以区分类型。本文提出一种自动化痤疮识别与分类系统，采用机器学习方法对六类痤疮进行分类，以辅助皮肤科医生诊断。预处理阶段包括对比度增强、平滑滤波及RGB到L*a*b色彩空间转换，以降噪并提升分类精度。随后使用基于k-means聚类的分割方法提取病灶区域，并结合灰度共生矩阵（GLCM）与统计特征进行特征提取。最后，五种机器学习分类器用于分类任务。实验结果表明，随机森林（RF）达到最高准确率98.50%，优于现有先进方法。
原文摘要 · Abstract (English)
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.
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