Bazdar Amir, Hatamian Amir, Ostadieh Javad, Nourinia Javad, Ghobadi Changiz, Mostafapour Ehsan
Department of Electrical and Computer Engineering, Urmia University, Urmia, Iran.
Department of Electrical Engineering, Islamic Azad University, Urmia Brach, Urmia, Iran.
J Med Signals Sens. 2023 May 29;13(2):165-172. doi: 10.4103/jmss.jmss_145_21. eCollection 2023 Apr-Jun.
It has been a long time since we use magnetic resonance imaging (MRI) to detect brain diseases and many useful techniques have been developed for this task. However, there is still a potential for further improvement of classification of brain diseases in order to be sure of the results. In this research we presented, for the first time, a non-linear feature extraction method from the MRI sub-images that are obtained from the three levels of the two-dimensional Dual tree complex wavelet transform (2D DT-CWT) in order to classify multiple brain disease. After extracting the non-linear features from the sub-images, we used the spectral regression discriminant analysis (SRDA) algorithm to reduce the classifying features. Instead of using the deep neural networks that are computationally expensive, we proposed the Hybrid RBF network that uses the k-means and recursive least squares (RLS) algorithm simultaneously in its structure for classification. To evaluate the performance of RBF networks with hybrid learning algorithms, we classify nine brain diseases based on MRI processing using these networks, and compare the results with the previously presented classifiers including, supporting vector machines (SVM) and K-nearest neighbour (KNN). Comprehensive comparisons are made with the recently proposed cases by extracting various types and numbers of features. Our aim in this paper is to reduce the complexity and improve the classifying results with the hybrid RBF classifier and the results showed 100 percent classification accuracy in both the two class and the multiple classification of brain diseases in 8 and 10 classes. In this paper, we provided a low computational and precise method for brain MRI disease classification. the results show that the proposed method is not only accurate but also computationally reasonable.
我们使用磁共振成像（MRI）来检测脑部疾病已有很长时间了，并且已经为此任务开发了许多有用的技术。然而，为了确保结果的准确性，脑部疾病分类仍有进一步改进的潜力。在本研究中，我们首次提出了一种从MRI子图像中提取非线性特征的方法，这些子图像是通过二维双树复小波变换（2D DT-CWT）的三个级别获得的，用于对多种脑部疾病进行分类。从子图像中提取非线性特征后，我们使用谱回归判别分析（SRDA）算法来减少分类特征。我们没有使用计算成本高昂的深度神经网络，而是提出了混合径向基函数（RBF）网络，该网络在其结构中同时使用k均值和递归最小二乘（RLS）算法进行分类。为了评估具有混合学习算法的RBF网络的性能，我们基于MRI处理使用这些网络对九种脑部疾病进行分类，并将结果与之前提出的分类器进行比较，包括支持向量机（SVM）和K近邻（KNN）。通过提取各种类型和数量的特征，与最近提出的案例进行了全面比较。本文的目的是使用混合RBF分类器降低复杂度并提高分类结果，结果表明在脑部疾病的二类和八类及十类多分类中，分类准确率均达到了100%。在本文中，我们为脑部MRI疾病分类提供了一种低计算量且精确的方法。结果表明，所提出的方法不仅准确，而且计算合理。
