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.