Search research articles

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

BERT based sentiment analysis of consumer hesitancy toward solar energy adoption.

Scientific reports·2026

Same author

Forecasting compressive strength of concrete containing rice husk ash using various machine learning algorithms.

Scientific reports·2025

Same author

Hybrid machine learning models for predicting the tensile strength of reinforced concrete incorporating nano-engineered and sustainable supplementary cementitious materials.

Scientific reports·2025

Same author

Application of machine learning techniques to predict the compressive strength of steel fiber reinforced concrete.

Scientific reports·2025

Same author

Potato leaves disease classification based on generalized Jones polynomials image features.

MethodsX·2025

Same author

The social organization of the Asian weaver ant colonies: A natural enemy novel sub-castes worker's functional activity findings.

PloS one·2025

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Nov 27, 2025

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

MRI Brain Classification Using the Quantum Entropy LBP and Deep-Learning-Based Features.

Ali M Hasan¹, Hamid A Jalab², Rabha W Ibrahim^3,4

¹College of Medicine, Al-Nahrain University, Baghdad 10001, Iraq.

Entropy (Basel, Switzerland)

|December 8, 2020

Summary

Early brain tumor detection using advanced medical imaging is crucial for patient recovery. This study introduces Quantum Entropy Local Binary Patterns (QELBP) combined with deep learning, achieving 98.80% accuracy in MRI brain scan classification.

Keywords:

MRI classification deep learning fractional calculus quantum calculus quantum entropy

More Related Videos

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Related Experiment Videos

Last Updated: Nov 27, 2025

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Area of Science:

Medical Imaging Analysis
Quantum Calculus Applications
Machine Learning in Healthcare

Background:

Early brain tumor detection significantly improves patient prognosis.
Medical imaging technologies are integral to modern diagnosis and treatment.
Existing feature extraction methods can be enhanced for better classification.

Purpose of the Study:

To generalize entropy difference using quantum calculus.
To extend Local Binary Patterns (LBP) into Quantum Entropy LBP (QELBP).
To improve MRI brain scan classification accuracy by combining QELBP and deep learning features.

Main Methods:

Generalization of entropy difference using quantum calculus.
Development of Quantum Entropy Local Binary Patterns (QELBP) feature extraction.
Integration of QELBP and deep learning (DL) features for MRI brain scan classification.
Utilizing a long short-term memory (LSTM) network for classification.

Main Results:

A novel QELBP feature extraction method was proposed.
Combining QELBP and DL features enhanced classification performance.
The combined feature approach achieved a maximum accuracy of 98.80% on a dataset of 154 MRI brain scans.
Experimental results validated the effectiveness of feature combination.

Conclusions:

The proposed QELBP-DL approach significantly improves MRI brain tumor classification accuracy.
Combining extracted features is a viable strategy for enhancing diagnostic performance.
This method holds promise for earlier and more accurate brain tumor detection.