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首页> 外文期刊>The mediterranean journal of electronics and communications >IMPROVED DEMPSTER AND SHAFER THEORY TO FUSE FUZZY INFERENCE SYSYEM, NEURAL NETWROKS AND CNN ENDOCARDIAL EDGE DETECTION
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IMPROVED DEMPSTER AND SHAFER THEORY TO FUSE FUZZY INFERENCE SYSYEM, NEURAL NETWROKS AND CNN ENDOCARDIAL EDGE DETECTION

机译：改进的模糊理论和融合理论，用于融合模糊推理系统，神经网络和CNN内缘检测

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摘要

Data fusion is an important tool for improving the performance of a detection system when more than one classifier is available. The reasoning logic Dempster-Shafer evidence theory for fusion is similar to that of humans. This paper applies a data fusion method which is based on improvements to the Dempster-Shafer theory, to echocardiographic images to increase the detection accuracy of the endocardial edges. In this paper, edge detectors employing a fuzzy inference system, artificial neural networks and cellular neural networks are designed and implemented. The Improved Dempster-Shafer evidence fusion algorithm is applied to combine the three classifiers. Results of computational experiments show promising results.
机译：当有多个分类器可用时，数据融合是提高检测系统性能的重要工具。融合的推理逻辑Dempster-Shafer证据理论与人类相似。本文将基于改进的Dempster-Shafer理论的数据融合方法应用于超声心动图图像，以提高心内膜边缘的检测精度。本文设计并实现了采用模糊推理系统，人工神经网络和细胞神经网络的边缘检测器。改进的Dempster-Shafer证据融合算法被用于组合三个分类器。计算实验结果显示出令人鼓舞的结果。

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