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首页> 外文期刊>International Journal of Computer Trends and Technology >An Approach to Medical Image Classification Using Neuro Fuzzy Logic and ANFIS Classifier
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An Approach to Medical Image Classification Using Neuro Fuzzy Logic and ANFIS Classifier

机译:基于神经模糊逻辑和ANFIS分类器的医学图像分类方法

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It is a challenging task to analyze medical images because there are very minute variations & larger data set for analysis. It is a quite difficult to develop an automated recognition system which could process on a large information of patient and provide a correct estimation. The conventional method in medicine for brain MR images classification and tumor detection is by human inspection. Fuzzy logic technique is more accurate but it fully depends on expert knowledge, which may not always available. Here we extract the feature using PCA and after that training using the ANFIS tool. The performance of the ANFIS classifier was evaluated in terms of training performance and classification accuracy. Here the result confirmed that the proposed ANFIS classifier with accuracy greater than 90 percentage has potential in detecting the tumors. This paper describes the proposed strategy to medical image classification of patient’s MRI scan images of the brain.
机译:分析医学图像是一项艰巨的任务,因为存在非常微小的变化,并且需要更大的数据集进行分析。开发一种自动识别系统非常困难,该系统可以处理大量患者信息并提供正确的估计。医学上用于脑部MR图像分类和肿瘤检测的常规方法是通过人工检查。模糊逻辑技术更准确,但它完全取决于专家知识,而这些知识可能并不总是可用。在这里,我们使用PCA提取特征,然后使用ANFIS工具进行训练。 ANFIS分类器的性能是根据训练性能和分类准确性进行评估的。此处的结果证实,所提出的ANFIS分类器的准确性大于90%,具有检测肿瘤的潜力。本文介绍了针对患者的大脑MRI扫描图像进行医学图像分类的拟议策略。

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