MRI brain classification using the quantum entropy LBP and deep-learning-based features
Journal article
Authors | Hasan, Ali M., Jalab, Hamid A., Ibrahim, Rabha W., Meziane, Farid, AL-Shamasneh, Ala’a R. and Obaiys, Suzan J. |
---|---|
Abstract | 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 di erence defined in terms of Marsaglia formula (usually used to describe two di erent 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. |
Keywords | quantum calculus; fractional calculus; quantum entropy; deep learning; MRI classification |
Year | 2020 |
Journal | Entropy |
Journal citation | 22 (9), p. 1033 |
Publisher | MDPI AG |
ISSN | 1099-4300 |
Digital Object Identifier (DOI) | https://doi.org/10.3390/e22091033 |
Web address (URL) | http://hdl.handle.net/10545/625210 |
https://creativecommons.org/licenses/by/4.0/ | |
hdl:10545/625210 | |
Publication dates | 15 Sep 2020 |
Publication process dates | |
Deposited | 25 Sep 2020, 14:55 |
Accepted | 11 Sep 2020 |
Contributors | Al-Nahrain University, Baghdad 10001, Iraq, University of Malaya, Kuala Lumpur 50603, Malaysia, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam, University of Derby and Heriot-Watt University Malaysia, Putrajaya 62200, Malaysia |
File | File Access Level Open |
File | File Access Level Open |
https://repository.derby.ac.uk/item/92y28/mri-brain-classification-using-the-quantum-entropy-lbp-and-deep-learning-based-features
Download files
110
total views17
total downloads52
views this month1
downloads this month