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Open AccessArticle

MRI Brain Classification Using the Quantum Entropy LBP and Deep-Learning-Based Features

1
College of Medicine, Al-Nahrain University, Baghdad 10001, Iraq
2
Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
3
Informetrics Research Group, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam
4
Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam
5
Data science Research Centre, University of Derby, Kedleston Rd, Derby DE22 1GB, UK
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School of Mathematical & Computer Sciences, Heriot-Watt University Malaysia, Putrajaya 62200, Malaysia
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(9), 1033; https://doi.org/10.3390/e22091033
Received: 21 July 2020 / Revised: 10 September 2020 / Accepted: 11 September 2020 / Published: 15 September 2020
Brain tumor detection at early stages can increase the chances of the patient’s recovery after treatment. In the last decade, we have noticed a substantial development in the medical imaging technologies, and they are now becoming an integral part in the diagnosis and treatment processes. In this study, we generalize the concept of entropy difference defined in terms of Marsaglia formula (usually used to describe two different figures, statues, etc.) by using the quantum calculus. Then we employ the result to extend the local binary patterns (LBP) to get the quantum entropy LBP (QELBP). The proposed study consists of two approaches of features extractions of MRI brain scans, namely, the QELBP and the deep learning DL features. The classification of MRI brain scan is improved by exploiting the excellent performance of the QELBP–DL feature extraction of the brain in MRI brain scans. The combining all of the extracted features increase the classification accuracy of long short-term memory network when using it as the brain tumor classifier. The maximum accuracy achieved for classifying a dataset comprising 154 MRI brain scan is 98.80%. The experimental results demonstrate that combining the extracted features improves the performance of MRI brain tumor classification. View Full-Text
Keywords: quantum calculus; fractional calculus; quantum entropy; deep learning; MRI classification quantum calculus; fractional calculus; quantum entropy; deep learning; MRI classification
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MDPI and ACS Style

Hasan, A.M.; Jalab, H.A.; Ibrahim, R.W.; Meziane, F.; AL-Shamasneh, A.R.; Obaiys, S.J. MRI Brain Classification Using the Quantum Entropy LBP and Deep-Learning-Based Features. Entropy 2020, 22, 1033. https://doi.org/10.3390/e22091033

AMA Style

Hasan AM, Jalab HA, Ibrahim RW, Meziane F, AL-Shamasneh AR, Obaiys SJ. MRI Brain Classification Using the Quantum Entropy LBP and Deep-Learning-Based Features. Entropy. 2020; 22(9):1033. https://doi.org/10.3390/e22091033

Chicago/Turabian Style

Hasan, Ali M.; Jalab, Hamid A.; Ibrahim, Rabha W.; Meziane, Farid; AL-Shamasneh, Ala’a R.; Obaiys, Suzan J. 2020. "MRI Brain Classification Using the Quantum Entropy LBP and Deep-Learning-Based Features" Entropy 22, no. 9: 1033. https://doi.org/10.3390/e22091033

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