Detection and Classification of Brain Tumor in MRI Images Using Wavelet Transform and Convolutional Neural Network

Main Article Content

Ahmad M. Sarhan

Abstract

A brain tumor is a mass of abnormal cells in the brain. Brain tumors can be benign or malignant. Conventional diagnosis of a brain tumor by the radiologist, is done by examining a set of images produced by magnetic resonance imaging (MRI). Many computer-aided detection (CAD) systems have been developed in order to help the radiologist reach his goal of correctly classifying the MRI image. Convolutional neural networks (CNNs) have been widely used in the classification of medical images. This paper presents a novel CAD technique for the classification of brain tumors in MRI images The proposed system extracts features from the brain MRI images by utilizing the strong energy compactness property exhibited by the Discrete Wavelet transform (DWT). The Wavelet features are then applied to a CNN to classify the input MRI image. Experimental results indicate that the proposed approach outperforms other commonly used methods and gives an overall accuracy of 98.5%.

Keywords:
Brain tumor, cancer detection, wavelet transform, Convolutional Neural Networks (CNNs), Magnetic Resonance Imaging (MRI).

Article Details

How to Cite
Sarhan, A. M. (2020). Detection and Classification of Brain Tumor in MRI Images Using Wavelet Transform and Convolutional Neural Network. Journal of Advances in Medicine and Medical Research, 32(12), 15-26. https://doi.org/10.9734/jammr/2020/v32i1230539
Section
Original Research Article

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