Volume 12 Number 2 (Jun. 2020)
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IJCEE 2020 Vol.12(2): 72-82 ISSN: 1793-8163
DOI: 10.17706/IJCEE.2020.12.2.72-82

Cardiovascular Disease Detection Using MRI Data with Deep Learning Approach

Mohammed Zakariah, Khaled AlShalfan
Abstract—Cardiovascular disease prediction is very critical area of research. In this work we tried to measure the left ventricular volume which plays an important role in cardiac arrests. In this work we contributed in data pre-processing of the CMR images then applied deep neural network. The data used in this work was Sunnybrook Cardiac Dataset (SCD) and Cardiac Atlas Project (CAP). It is LV CMR images dataset. Convolution neural network and maxpooling with ADAM activation function was applied for the proposed architecture. The results are at very initial stages and further enhancement could be done in the future by applying more efficient pre-processing techniques.

Index Terms—Cardiovascular disease, deep learning, medical image processing, left ventricular ejection fraction.

Mohammed Zakariah is with College of Computer and Information Science, King Saud University, Riyadh, Saudi Arabia. Khaled AlShalfan is with College of Computer and Information Science, Imam Muhammad ibn Saud Islamic University, Riyadh, Saudi Arabia.

Cite:Mohammed Zakariah, Khaled AlShalfan, "Cardiovascular Disease Detection Using MRI Data with Deep Learning Approach," International Journal of Computer and Electrical Engineering vol. 12, no. 2, pp. 72-82, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

ISSN: 1793-8163 (Print)
Abbreviated Title: Int. J. Comput. Electr. Eng.
Frequency: Quarterly
Editor-in-Chief: Prof. Yucong Duan
Abstracting/ Indexing: EI (INSPEC, IET), Ulrich's Periodicals Directory, Google Scholar, EBSCO, ProQuest, and Electronic Journals Library
E-mail: ijcee@iap.org

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