MRI brain classification using the quantum entropy LBP and deep-learning-based features

Journal article


Hasan, Ali M., Jalab, Hamid A., Ibrahim, Rabha W., Meziane, Farid, AL-Shamasneh, Ala’a R. and Obaiys, Suzan J. 2020. MRI brain classification using the quantum entropy LBP and deep-learning-based features. Entropy. 22 (9), p. 1033. https://doi.org/10.3390/e22091033
AuthorsHasan, 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.

Keywordsquantum calculus; fractional calculus; quantum entropy; deep learning; MRI classification
Year2020
JournalEntropy
Journal citation22 (9), p. 1033
PublisherMDPI AG
ISSN1099-4300
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 dates15 Sep 2020
Publication process dates
Deposited25 Sep 2020, 14:55
Accepted11 Sep 2020
ContributorsAl-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
Permalink -

https://repository.derby.ac.uk/item/92y28/mri-brain-classification-using-the-quantum-entropy-lbp-and-deep-learning-based-features

Download files

  • 5
    total views
  • 0
    total downloads
  • 0
    views this month
  • 0
    downloads this month

Export as

Related outputs

A review of the generation of requirements specification in natural language using objects UML models and domain ontology
Abdalazeima, Alaa and Meziane, Farid 2021. A review of the generation of requirements specification in natural language using objects UML models and domain ontology. Procedia Computer Science. 189, pp. 328-334. https://doi.org/10.1016/j.procs.2021.05.102
Credit Risk Prediction for Peer-To-Peer Lending Platforms: An Explainable Machine Learning Approach
Swee, C.P., Labadin, J. and Meziane, F. 2022. Credit Risk Prediction for Peer-To-Peer Lending Platforms: An Explainable Machine Learning Approach. Journal of Computing and Social Informatics. 1 (2), pp. 1-16. https://doi.org/10.33736/jcsi.4761.2022
DCOPA: a distributed clustering based on objects performances aggregation for hierarchical communications in IoT applications
Mir, F. and Meziane, F. 2022. DCOPA: a distributed clustering based on objects performances aggregation for hierarchical communications in IoT applications. Cluster Computing. pp. 1-22. https://doi.org/10.1007/s10586-022-03741-w
Botnet detection used fast-flux technique, based on adaptive dynamic evolving spiking neural network algorithm
Almomani, Ammar, Nawasrah, Ahmad Al, Alauthman, Mohammad, Betar, Mohammed Azmi Al and Meziane, Farid 2021. Botnet detection used fast-flux technique, based on adaptive dynamic evolving spiking neural network algorithm. International Journal of Ad Hoc and Ubiquitous Computing. 36 (1), p. 50. https://doi.org/10.1016/j.cosrev.2020.100305
Arabic machine translation: A survey of the latest trends and challenges
Ameur, M.S.H., Meziane, Farid and Guessoum, Ahmed 2020. Arabic machine translation: A survey of the latest trends and challenges. Computer Science Review. 38, p. 100305. https://doi.org/10.1016/j.cosrev.2020.100305