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Content-based image retrieval techniques for the analysis of dermatological lesions using particle swarm optimization technique

机译:基于粒子群优化技术的基于内容的图像检索技术在皮肤病学分析中的应用

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摘要

This method presents extraction of effective color and shape features for the analysis of dermatology images. We employ three phases of operation in order to perform efficient retrieval of images of skin lesions. Our proposed algorithm used color and shape feature vectors and the features are normalized using Min-Max normalization. Particle swarm optimization (PSO) technique for multi-class classification is used to converge the search space more efficiently. The results using receiver operating characteristic (ROC) curve proved that the proposed architecture is highly contributed to computer-aided diagnosis of skin lesions. Experiments on a set of 1450 images yielded a specificity of 98.22% and a sensitivity of 94%. Our empirical evaluation has a superior retrieval and diagnosis performance when compared to the performance of other works. We present explicit combinations of feature vectors corresponding to healthy and lesion skin. (C) 2015 Elsevier B.V. All rights reserved.
机译:该方法提供了有效颜色和形状特征的提取,用于皮肤病学图像分析。我们采用三个阶段的操作,以执行皮肤病变图像的有效检索。我们提出的算法使用颜色和形状特征向量,并且使用最小-最大归一化对特征进行归一化。用于多类分类的粒子群优化(PSO)技术用于更有效地收敛搜索空间。使用接收器工作特性(ROC)曲线的结果证明,该建议的体系结构对皮肤病变的计算机辅助诊断有很大贡献。在一组1450张图像上进行的实验得出的特异性为98.22%,灵敏度为94%。与其他作品相比,我们的经验评估具有出色的检索和诊断性能。我们提出了与健康和病变皮肤相对应的特征向量的显式组合。 (C)2015 Elsevier B.V.保留所有权利。

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