A primary morphological classifier for skin lesion images

• Main Authors: Macatangay, Jules Matthew A., Ruiz, Conrado R., Usatine, Richard P.
• Format: text
• Published: Animo Repository 2017
• Subjects: Skin—Diseases; Computer vision; Machine learning; Image segmentation; Nearest neighbor analysis (Statistics); Computer Sciences
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/2695
• Institution: De La Salle University

Classifying skin lesions, abnormal changes in skin, into their morphologies is the first step in diagnosing skin diseases. In dermatology, morphology is a categorization of a skin lesion's structure and appearance. Rather than directly classifying skin diseases, this research aims to explore classifying skin lesion images into primary morphologies. For preprocessing, k-means clustering for image segmentation and illumination equalization were applied. Additionally, features utilized considered color, texture, and shape. For classification, k-Nearest Neighbors, Decision Trees, Multilayer Perceptron, and Support Vector Machines were used. To evaluate the prototype, 10-fold cross validation was applied over a dataset assembled from online resources. In experimentation, the morphologies considered were macule, nodule, papule, and plaque. Moreover, different feature subsets were tested through feature selection experiments. Experimental results on the 4-class and 3-class tests show that of the classifiers selected, Decision Trees were best, having a Cohen's kappa of 0.503 and 0.558 respectively. © 2017 Computer Science Research Notes.