An Approach for Classifying Skin Diseases Using Texture Analysis

Early diagnosis of skin diseases, mostly in developing countries, is necessary since it has serious impact on people’s quality of life. In this study, an automated model is proposed to classify skin diseases for dermoscopic images using texture analysis. There are some challenges for automating the classification of skin lesions such as existence of hair in the lesion, dark and highlight regions in dermoscopic image and different types of skin tone. In this study, these challenges are addressed with class imbalance issue, an internecine and common problem of skin disease image datasets. By combining different techniques or algorithms an effective approach is introduced to allay those challenges. The performance of the proposed approach is tested for two dermatological skin conditions viz. Solar Lentigo, Lentigo Simplex using k-th nearest neighbor (kNN) and support vector machine (SVM) as classifier.