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首页> 外文会议>IEEE International Conference on BioInformatics and BioEngineering >A Multiple Expert-Based Melanoma Recognition System for Dermoscopic Images of Pigmented Skin Lesions
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A Multiple Expert-Based Melanoma Recognition System for Dermoscopic Images of Pigmented Skin Lesions

机译：一种多种专家的黑色素瘤识别系统，用于色素皮肤病变的Dermoscopic图像

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This paper presents an integrated decision support system for an automated melanoma recognition of dermoscopic images based on multiple expert fusion. In this context, the ultimate aim is to support decision making by predicting image categories (e.g., melanoma, benign and dysplastic nevi) by combining outputs from different classifiers. A fast and automatic segmentation method to detect the lesion from the background healthy skin is proposed and lesion-specific local color and texture-related features are extracted. For the classification, combining experts which are classifiers with different structures, are examined as alternative solution instead of an individual classifier. In this approach, probabilistic outputs of the experts are combined based on the combination rules that are derived by following Bayes' theorem. The category label with the highest confidence score is considered to be the class of a test image. Experimental results on a collection of 358 dermoscopic images demonstrate the effectiveness of the proposed expert fusion-based approach.
机译：本文提出了一种基于多个专家融合的Dermospic图像自动黑色素瘤的综合决策支持系统。在这种情况下，最终目的是通过将来自不同分类器的输出组合来支持图像类别（例如，黑色素瘤，良性和消化奈维）来支持决策。提出了一种快速和自动分段方法，以检测来自背景健康皮肤的病变，提取病变特异性局部颜色和纹理相关的特征。对于分类，将是具有不同结构的分类器的组合专家被检查为替代解决方案而不是单独的分类器。在这种方法中，基于以下贝叶斯定理所衍生的组合规则，组合专家的概率产出。具有最高置信度分数的类别标签被认为是测试图像的类。 358个Dermoscopic图像集合的实验结果表明了拟议的专家融合方法的有效性。

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