An Efficient Methodology for Brain MRI Classification Based on DWT and Convolutional Neural Network

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...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-11, Vol.21 (22), p.7480
Main Authors: Fayaz, Muhammad, Torokeldiev, Nurlan, Turdumamatov, Samat, Qureshi, Muhammad Shuaib, Qureshi, Muhammad Bilal, Gwak, Jeonghwan
Format: Article
Language:English
Subjects:
Algorithms
Automation
Brain
Classification
convolutional neural network
Deep learning
Discrete Wavelet Transform
Image classification
Machine learning
Magnetic resonance imaging
Medical imaging
MRI
Neural networks
Preprocessing
Quantitative analysis
Wavelet transforms
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21227480