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A Review on Image Classification Techniques to classify Neurological Disorders of brain MRI

机译:影像分类技术对脑部MRI神经系统疾病进行分类的研究进展

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Neurological disorders have more than 600 brain disease. Therefore it is very complicated task to detect and classify the brain MRI data. To classify the brain MR image by many classification techniques such as K-nearest neighbor (KNN), decision tree (DT), Support vector machine (SVM), neural network and convolutional neural network, we have study and compered K-nearest neighbor, support vector machine, decision tree, neural network and convolution neural network . In this review paper we explain which classification technique is better for detection of brain MRI data set. The detection for normal and abnormal brain MRI, the CNN improve the accuracy as compare to other classification.
机译:神经系统疾病有600多种脑部疾病。因此,检测和分类大脑MRI数据是非常复杂的任务。为了通过多种分类技术(例如K近邻(KNN),决策树(DT),支持向量机(SVM),神经网络和卷积神经网络)对大脑MR图像进行分类,我们研究了K近邻,并对其进行了修正,支持向量机,决策树,神经网络和卷积神经网络。在这篇综述文章中,我们解释了哪种分类技术更适合检测脑部MRI数据集。与其他分类相比,CNN可以检测正常和异常的脑部MRI,从而提高了准确性。

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