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

Ali M. Hasan, Hamid A. Jalab, Rabha W. Ibrahim*, Farid Meziane, Ala'a R. AL-Shamasneh, Suzan J. Obaiys

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)
47 Downloads (Pure)


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.

Original languageEnglish
Article number1033
Issue number9
Early online date15 Sept 2020
Publication statusPublished - Sept 2020


  • Deep learning
  • Fractional calculus
  • MRI classification
  • Quantum calculus
  • Quantum entropy

ASJC Scopus subject areas

  • General Physics and Astronomy


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