Detection of Common Types of Eczema Using Gray Level Co-occurrence Matrix and Support Vector Machine

Abstract

Many people are being affected by eczema around the world. In the Philippines, the most common types of eczema are atopic dermatitis, contact dermatitis, and nummular dermatitis. This study covered these three types and detected them by applying image processing techniques, Gray Level Co-occurrence Matrix, and the classifier Support Vector Machine. The designed prototype is to capture a section of the skin where eczema can be present and send the image to the software for skin region detection, eczema region detection, and feature extractions. In skin region detection, the YCbCr color model identifies the skin's color to discard the non-skin pixels and detect the skin pixels, allowing isolation of those pixels. The eczema region detection uses the CIELAB color model and K-means clustering to extract eczema on the image. The feature extractions have color features composed of RGB, HSV, and YCbCr color models and texture features consisting of contrast, homogeneity, energy, and correlation of GLCM. Then the software will classify the acquired image as healthy, atopic, contact, or nummular using SVM. Next to the testing process, the results are obtained and plotted in a confusion matrix. After analyzing the results, the computed overall accuracy of the system was 83.33%.