عنوان مقاله [English]
In this study, a new algorithm for dermoscopy image classification into two types of malignant and benign is presented. At first, one preprocessing step to remove noise and also enhance image quality is performed. After that using Otsu thresholding, the lesion is separated from the healthy area. Then color and shape features are extracted from the segmented image. The colored features based on statistical moments of quantized grayscale and quantized color histogram are defined. These features demonastrare distribution of color components. Moreover, the shape features are extracted information of the segmented regions with two scenarios. In the first scenario, the features are represented the expantion of region and in the second scenario, the features are represented the edge variations of the extracted regions. Finally, the classification procedure is performed using K-Neasert Neighbor (KNN), Decision Tree, Support Vector Machine (SVM) and Adaboost. The proposed algorithm is evaluated on a standard database consisting of 200 images. The results show that classification using Adaboost classifier obtains the precision rate, accuracy rate and sensitivity rate of 96%, 96.7% and 95%, respectively.
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