Identification of Acne Vulgaris Type in Facial Acne Images Using GLCM Feature Extraction and Extreme Learning Machine Algorithm

Abstract

Acne vulgaris or acne is a common inflammatory pilosebaceous condition that affects up to 90% of teenagers, begins during adolescent years, and often persists into adulthood. Acne vulgaris, especially on the face, has a major impact on the emotional, social and psychological health of patients. In treating acne, it is necessary to identify the exact type of acne. The manual method is considered less effective, so it is proposed an automatic method using a computer, which uses image processing techniques. This research was conducted to identify the types of acne on facial acne images. The methods used are K-Means Clustering for segmentation, Gray Level Co-occurrence Matrix (GLCM) for feature extraction, and Extreme Learning Machine (ELM) for classification. The dataset is 100 images and consists of 3 classes, namely Nodules, Papules and Pustules. Testing is done in two stages, namely testing 2 classes (Nodules and Papules), followed by testing 3 classes (Nodules, Papules and Pustules). Testing of 2 classes produces the highest accuracy of 95,24% and testing of 3 classes produces the highest accuracy of 80%.