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.
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15.9.2020
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