Department of Computer Science, University of Central Asia, 310 Lenin Street, Naryn 722918, Kyrgyzstan.
Department of Mathematics and Natural Sciences, University of Central Asia, Khorog 736, Tajikistan.
Sensors (Basel). 2021 Nov 10;21(22):7480. doi: 10.3390/s21227480.
In this paper, a model based on discrete wavelet transform and convolutional neural network for brain MR image classification has been proposed. The proposed model is comprised of three main stages, namely preprocessing, feature extraction, and classification. In the preprocessing, the median filter has been applied to remove salt-and-pepper noise from the brain MRI images. In the discrete wavelet transform, discrete Harr wavelet transform has been used. In the proposed model, 3-level Harr wavelet decomposition has been applied on the images to remove low-level detail and reduce the size of the images. Next, the convolutional neural network has been used for classifying the brain MR images into normal and abnormal. The convolutional neural network is also a prevalent classification method and has been widely used in different areas. In this study, the convolutional neural network has been used for brain MRI classification. The proposed methodology has been applied to the standard dataset, and for performance evaluation, we have used different performance evaluation measures. The results indicate that the proposed method provides good results with 99% accuracy. The proposed method results are then presented for comparison with some state-of-the-art algorithms where simply the proposed method outperforms the counterpart algorithms. The proposed model has been developed to be used for practical applications.
本文提出了一种基于离散小波变换和卷积神经网络的脑磁共振图像分类模型。该模型由预处理、特征提取和分类三个主要阶段组成。在预处理阶段，应用中值滤波器去除脑 MRI 图像中的椒盐噪声。在离散小波变换中，使用了离散哈雷小波变换。在提出的模型中，对图像进行了 3 级哈雷小波分解，以去除低水平细节并减小图像的大小。接下来，卷积神经网络用于将脑磁共振图像分类为正常和异常。卷积神经网络也是一种流行的分类方法，已广泛应用于不同领域。在这项研究中，卷积神经网络用于脑 MRI 分类。所提出的方法已应用于标准数据集，并且为了进行性能评估，我们使用了不同的性能评估指标。结果表明，该方法的准确率达到了 99%。然后将提出的方法的结果与一些最先进的算法进行了比较，结果表明，所提出的方法优于对照算法。所提出的模型旨在用于实际应用。
