MRI-BASED BRAIN TUMOR IMAGE CLASSIFICATION USING DEEP LEARNING

Doaa Ayed  Mohammed; Sarah A. Abed; Mohammed  Maithem

doi:10.30572/2018/KJE/170236

Authors

Doaa Ayed Mohammed College of Information Technology, University of Babylon, Babylon, Iraq
Sarah A. Abed College of Information Technology, University of Babylon, Babylon, Iraq
Mohammed Maithem College of Information Technology, University of Babylon, Babylon, Iraq

DOI:

https://doi.org/10.30572/2018/KJE/170236

Keywords:

Deep learning (DL), Image Classification, Convolutional Neural Networks (CNNs), Magnetic Resonance Imaging (MRI), Brain Tumors, Tumors

Abstract

Modernly speaking, reviewing large numbers of Magnetic Resonance Imaging (MRI) images and manually discovering a brain tumor by a person is a slow and inaccurate process. It may have effects on the correct medical treatment of the patient. Additionally, it could be a slow and laborious task due to the numerous amounts of image datasets involved. Because brain tumors appear similarly to healthy tissue, tumor region segmentation can be difficult. Therefore, there is a need for a high-quality automatic tumor detection system. CNNs are one type of deep learning technique which are often used for image recognition and image classification tasks currently. CNNs are also commonly used to identify Brain Tumors. In our research, we proposed a CNN model for the purpose of classifying images from MRI scans of brains into two classes (Normal or Tumor). Our proposed model was able to achieve a recall of 97.51%, accuracy of 97.889%, F1-score of 97.84%, precision of 98.18%, specificity of 97.62% and an AUC of 97.57%. Our CNN model will help doctors to find brain tumors in MRI images with great efficiency , therefore, greatly increasing the amount of time saved when treating patients

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