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dc.contributor.authorHasan, Ali M.
dc.contributor.authorJalab, Hamid A.
dc.contributor.authorIbrahim, Rabha W.
dc.contributor.authorMeziane, Farid
dc.contributor.authorAL-Shamasneh, Ala’a R.
dc.contributor.authorObaiys, Suzan J.
dc.date.accessioned2020-09-25T14:55:05Z
dc.date.available2020-09-25T14:55:05Z
dc.date.issued2020-09-15
dc.identifier.citationHasan, A.M., Jalab, H.A., Ibrahim, R.W., Meziane, F., AL-Shamasneh, A.A.R. and Obaiys, S.J., (2020). 'MRI brain classification using the quantum entropy LBP and deep-learning-based features'. Entropy, 22(9), pp. 1-12.en_US
dc.identifier.doi10.3390/e22091033
dc.identifier.urihttp://hdl.handle.net/10545/625210
dc.description.abstractBrain 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.en_US
dc.description.sponsorshipN/Aen_US
dc.language.isoenen_US
dc.publisherMDPI AGen_US
dc.relation.urlhttps://www.mdpi.com/1099-4300/22/9/1033/xmlen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectquantum calculusen_US
dc.subjectfractional calculusen_US
dc.subjectquantum entropyen_US
dc.subjectdeep learningen_US
dc.subjectMRI classificationen_US
dc.titleMRI brain classification using the quantum entropy LBP and deep-learning-based featuresen_US
dc.typeArticleen_US
dc.identifier.eissn1099-4300
dc.contributor.departmentAl-Nahrain University, Baghdad 10001, Iraqen_US
dc.contributor.departmentUniversity of Malaya, Kuala Lumpur 50603, Malaysiaen_US
dc.contributor.departmentTon Duc Thang University, Ho Chi Minh City 758307, Vietnamen_US
dc.contributor.departmentUniversity of Derbyen_US
dc.contributor.departmentHeriot-Watt University Malaysia, Putrajaya 62200, Malaysiaen_US
dc.identifier.journalEntropyen_US
dc.identifier.piie22091033
dc.source.journaltitleEntropy
dc.source.volume22
dc.source.issue9
dc.source.beginpage1033
dcterms.dateAccepted2020-09-11
refterms.dateFOA2020-09-25T14:55:06Z
dc.author.detail300775en_US


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