Pattern classification of dermoscopy images: A perceptually uniform model

Pattern classification of dermoscopy images is a challenging task of differentiating between benign melanocytic lesions and melanomas. In this paper, a novel pattern classification method based on color symmetry and multiscale texture analysis is developed to assist dermatologists' diagnosis. Our method aims to classify various tumor patterns using color–texture properties extracted in a perceptually uniform color space. In order to design an optimal classifier and to address the problem of multicomponent patterns, an adaptive boosting multi-label learning algorithm (AdaBoost.MC) is developed. Finally, the class label set of the test pattern is determined by fusing the results produced by boosting based on the maximum a posteriori (MAP) and robust ranking principles. The proposed discrimination model for multi-label learning algorithm is fully automatic and obtains higher accuracy compared to existing multi-label classification methods. Our classification model obtains a sensitivity (SE) of 89.28%, specificity (SP) of 93.75% and an area under the curve (AUC) of 0.986. The results demonstrate that our pattern classifier based on color–texture features agrees with dermatologists' perception.

► A novel AdaBoost.MC classifier was developed to solve the problem of multicomponent patterns.
► This classifier focused more on color and texture properties of lesions in a uniform color space.
► The overall system has shown good performance to model CASH rule for dermoscopy images.