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Computer Based Advanced Approach for MRI Image Classification Using Neural Network With The Texture Features Extracted

机译:提取纹理特征的神经网络基于计算机的MRI图像分类高级方法

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Now a days,In order to look at organs inside the bodies Magnetic Resonance Imaging (MRI)utilizes radio waves and large magnet. This paper mainly provides an magnificent approach for automatic detection and classification is presented. Image classification,reprocessing and feature extraction are the main approaches in proposed system. In the pre-processing stage we used M3 Filtering Algorithm are applied for the removal of noise,Texture for capturing the contents of the images for indexing,feature extraction technique is applied. Classification of MRI images is carried out in the categories of Brain, Heart, Hands, Legs images using Probabilistic SVM(Support Vector Machine), PNN(probabilistic Neural Network), BPNN(Back Propagation Neural Network) algorithms. The method was applied using 400 images are collected from Andhra Hospital, Vijayawada. An overall accuracy of 92% is achieved with the MRI image classification using SVM contrast with other algorithms.
机译:如今,为了查看体内的器官,磁共振成像(MRI)利用无线电波和大型磁铁。本文主要提供了一种宏伟的自动检测和分类方法。图像分类,再处理和特征提取是所提出系统的主要方法。在预处理阶段,我们使用了M3滤波算法来去除噪声,使用纹理捕获图像内容进行索引,并使用特征提取技术。 MRI图像的分类使用概率SVM(支持向量机),PNN(概率神经网络),BPNN(反向传播神经网络)算法在脑,心脏,手,腿图像中进行。使用从维杰亚瓦达(Vijayawada)的安德拉医院(Andhra Hospital)收集的400张图像应用了该方法。使用SVM与其他算法进行对比的MRI图像分类可实现92%的总体准确度。

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