Characterization of texture in image of skin lesions by support vector machine - Oliveira, Roberta B. [UNESP]; Caldas Jr., Carlos Roberto D. [UNESP]; Pereira, Aledir S. [UNESP]; Guido, Rodrigo C. [UNESP]; Araujo, Alex F. de; Tavares, João Manuel R. S.; Rossetti, Ricardo B. (My University, 2012)
Due to the increased incidence of skin cancer, computational methods based on intelligent approaches have been developed to aid dermatologists in the diagnosis of skin lesions. This paper proposes a method to classify texture in images, since it is an important feature for the successfully identification of skin lesions. For this is defined a feature vector, with the fractal dimension of images through the box-counting method (BCM), which is used with a SVM to classify the texture of the lesions in to non-irregular or irregular. With the proposed solution, we could obtain an accuracy of 72.84%. © 2012 AISTI.
Keywords: box-counting method; fractal dimension; intelligent system; machine learning; support vector machine; Box-counting method; Feature vectors; Skin cancers; Skin lesion; Dermatology; Fractal dimension; Image retrieval; Information systems; Intelligent systems; Learning systems; Support vector machines; Textures; Image texture