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A High Performance Algorithm to Diagnosis of Skin Lesions Deterioration in Dermatoscopic Images Using New Feature Extraction

机译:一种高性能算法在新特征提取时诊断皮肤病患者皮肤病差的劣化

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Differentiation of pigmented skin lesions is difficult even for expert. In previous work, we proposed an algorithm for segmentation the dermoscopic images. In this paper, a feature extraction based algorithm is proposed which diagnose benignity or malignancy of the pigmented skin lesions in dermatoscopic images, to develop the previous work. In the proposed scheme the shape features are extracted from the binary segmented image according to ABCD rule. Subsequentely, after tracing the obtained binary image with the original dermatoscopic image, color and texture features are achieved according the same rule. The obtained features (shape, color and texture) are normalized to reach a high performance. Finally, classification is performed using SVM classifier to diagnose the deterioration of pigmented skin lesions (benignity or melanoma). The experimental results show that the proposed approach has specificity 90.03%, sensitivity 79.89% and accuracy 84.09% and improves the related results in existing works.
机译:即使对于专家,色素皮肤病变的分化也是困难的。在以前的工作中,我们提出了一种用于分割Dermoscopic图像的算法。在本文中,提出了一种基于特征提取的算法,其诊断皮肤病图像中着色的皮肤病变的良性或恶性肿瘤,以开发先前的工作。在所提出的方案中,根据ABCD规则从二进制分段图像中提取形状特征。随后,在跟踪具有原始Dercercopic图像的所获得的二进制图像之后,根据相同的规则实现颜色和纹理特征。所获得的特征(形状,颜色和纹理)被归一化以达到高性能。最后,使用SVM分类器进行分类,以诊断着色皮肤病变的恶化(良性或黑色瘤)。实验结果表明,该方法具有90.03%的特异性,灵敏度79.89%,准确性为84.09%,提高了现有工程的相关结果。

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