Objective and cost-efficient approach for skin lesions classification

Summary form only given. We deal with developing methods for an objective and cost-efficient tool for diagnosing skin lesions based on digitized dermatoscopic color images. We define a segmentation approach by fuzzy region growing applied to the Karhunen-Loeve transform of the RGB color vectors to separate pigmented lesion from the surrounding healthy skin. Then, a set of 14 characteristics of the lesion, represented by a set of numbers called feature scores, is extracted from the binary mask of the lesion deduced by the segmentation step. The quality of the features is evaluated by applying several feature selection methods in order to eliminate redundant information and accelerate the further classification step. Results show that most selection methods allow one to reduce the feature set to dimension five, which permits considerable reduction of calculation time, without significant loss of information. Feeding the selected features to a multilayer perception classifier allows us to generate a computerized diagnosis, suggesting whether the lesion is benign or malignant. With this approach, for reasonably balanced training/testing sets, we obtain above 77% correct classification of the malignant and benign tumors on real skin images.