Automatic Segmentation And Classification Of Eczema Skin Lesions Using Supervised Learning

In this paper our aim is to develop a fully automated eczema skin lesion segmentation method. We have studied three supervised learning methods for segmentation of lesions: Support Vector Machine (SVM), Naïve Bayesian Classifier (NBC) and K-Nearest Neighbor (KNN). Two sets of images that are different in erythema severity levels (mild, moderate) are used for training the supervised classifiers. From the training images 108 features are extracted that are ranked using four feature ranking methods (standard deviation, T-statistical score, fisher scoring, and correlation coefficient) to obtain the most significant features. The performance of classification is investigated using green channel of RGB and CSN-I RGB color space. The performance of the different methods is assessed by comparing the segmented lesions with the gold standard segmented images. Based on these comparisons, it is observed that SVM classifier shows the best segmentation result having an accuracy of 84.43% whereas the accuracy of NBC and KNN is 82.77% and 83.53% respectively.