Iranian Journal of Biomedical Engineering (IJBME)
نشریه علمی مهندسی پزشکی زیستی

Brain Tumor Detection using Fusion of MRI and CT Scan Images based on Deep Learning Feature Extraction Methods

Document Type : Full Research Paper

Authors

Dorsa Jafarkhah Seighalani 1
Mehran Yazdi 2
Mohammad Faghihi 3

1 M.Sc. Graduated, Communication Engineering (System), School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran

2 Associate Professor, Department of Communication and Electronical Engineering, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran

3 Ph.D. Student, Signal and Image Processing Lab, Department of Communication and Electronical Engineering, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran

https://doi.org/10.22041/ijbme.2020.123852.1583
Abstract
Cancer is one of the most common diseases at the present time. Among different types of this disease, brain cancer has a high fatality rate and accurate and timely diagnosis of it, can have a major impact on the patient’s life. Doctors need MRI and CT scan of brain to diagnose this condition. A precise image processing technique can help the medical specialists and speed up the diagnosis process. Many methods have been proposed to recognize brain tumors in medical images; however their accuracies were not acceptable. In fact, low accuracy is a result of the similarities between brain and tumor tissue. In this paper we propose a tumor recognition method using fusion of MRI and CT Scan images. This method exploits a deep learning based feature extraction algorithm that helps to distinguish tumors from brain tissue. Tumor recognition and accuracy calculation is performed for three common types of brain tumors (glioma, meningioma, and pituitary tumor). Our results show a great improvement of performance in comparison to related works. 

Keywords

MRI Image
CT Scan Image
Image Fusion
Brain Cancer
Deep Learning
Neural Networks

Subjects

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Iranian Journal of Biomedical Engineering (IJBME)
Volume 14, Issue 4
Winter 2021
Pages 267-276

  • Receive Date 01 April 2020
  • Revise Date 03 September 2020
  • Accept Date 07 October 2020

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