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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Autores principales:	Muhammad Fayaz, Nurlan Torokeldiev, Samat Turdumamatov, Muhammad Shuaib Qureshi, Muhammad Bilal Qureshi, Jeonghwan Gwak
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	classification convolutional neural network discrete wavelet transform MRI Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/4d618fd6c4ae436cb0cb2fb397f5480a
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id	oai:doaj.org-article:4d618fd6c4ae436cb0cb2fb397f5480a
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spelling	oai:doaj.org-article:4d618fd6c4ae436cb0cb2fb397f5480a2021-11-25T18:56:47ZAn Efficient Methodology for Brain MRI Classification Based on DWT and Convolutional Neural Network10.3390/s212274801424-8220https://doaj.org/article/4d618fd6c4ae436cb0cb2fb397f5480a2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7480https://doaj.org/toc/1424-8220In 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.Muhammad FayazNurlan TorokeldievSamat TurdumamatovMuhammad Shuaib QureshiMuhammad Bilal QureshiJeonghwan GwakMDPI AGarticleclassificationconvolutional neural networkdiscrete wavelet transformMRIChemical technologyTP1-1185ENSensors, Vol 21, Iss 7480, p 7480 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	classification convolutional neural network discrete wavelet transform MRI Chemical technology TP1-1185
spellingShingle	classification convolutional neural network discrete wavelet transform MRI Chemical technology TP1-1185 Muhammad Fayaz Nurlan Torokeldiev Samat Turdumamatov Muhammad Shuaib Qureshi Muhammad Bilal Qureshi Jeonghwan Gwak An Efficient Methodology for Brain MRI Classification Based on DWT and Convolutional Neural Network
description	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.
format	article
author	Muhammad Fayaz Nurlan Torokeldiev Samat Turdumamatov Muhammad Shuaib Qureshi Muhammad Bilal Qureshi Jeonghwan Gwak
author_facet	Muhammad Fayaz Nurlan Torokeldiev Samat Turdumamatov Muhammad Shuaib Qureshi Muhammad Bilal Qureshi Jeonghwan Gwak
author_sort	Muhammad Fayaz
title	An Efficient Methodology for Brain MRI Classification Based on DWT and Convolutional Neural Network
title_short	An Efficient Methodology for Brain MRI Classification Based on DWT and Convolutional Neural Network
title_full	An Efficient Methodology for Brain MRI Classification Based on DWT and Convolutional Neural Network
title_fullStr	An Efficient Methodology for Brain MRI Classification Based on DWT and Convolutional Neural Network
title_full_unstemmed	An Efficient Methodology for Brain MRI Classification Based on DWT and Convolutional Neural Network
title_sort	efficient methodology for brain mri classification based on dwt and convolutional neural network
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/4d618fd6c4ae436cb0cb2fb397f5480a
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An Efficient Methodology for Brain MRI Classification Based on DWT and Convolutional Neural Network

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