Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Brain Imaging01:14

Brain Imaging

313
Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
313

You might also read

Related Articles

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

Sort by
Same author

A lightweight hybrid framework for real-time data refinement in resource-constrained underwater and underground wireless sensor networks.

Scientific reports·2026
Same author

A novel computational framework for tumor-specific T cell antigen identification using a deep neural network.

Journal of computer-aided molecular design·2026
Same author

Focusing on legal cases: Automatic classification of legal documents with sentence embeddings and deep learning models.

PloS one·2026
Same author

predALZ: An Ensemble Learning Framework for Identifying Genetic Biomarkers in Familial Alzheimer's Disease.

Current drug targets·2026
Same author

HybridTrust: on-device federated learning with crypto-agile security for legacy and quantum-safe medical devices.

Scientific reports·2026
Same author

Mathematical analysis and simulation of an ABC fractional Zika virus model with natural transform and stability assessment.

Science progress·2026

Related Experiment Video

Updated: Sep 11, 2025

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

40.3K

Enhanced MRI brain tumor detection using deep learning in conjunction with explainable AI SHAP based diverse and

Asif Rahman1, Maqsood Hayat2, Nadeem Iqbal1

  • 1Department of Computer Science, Abdul Wali Khan University Mardan, Mardan, Khyber-Pakhtunkhwa, Pakistan.

Scientific Reports
|August 12, 2025
PubMed
Summary

Magnetic Resonance Imaging (MRI) combined with advanced feature extraction methods like local Binary Patterns (LBP) and Convolutional Neural Networks (CNN) significantly improves brain tumor detection accuracy. This approach offers a precise, non-invasive diagnostic tool for better patient outcomes.

Keywords:
Brain tumorCNNLBPMRIMachine learningPNNRFSHAP analysis

More Related Videos

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
09:06

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

12.2K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K

Related Experiment Videos

Last Updated: Sep 11, 2025

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

40.3K
Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
09:06

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

12.2K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Computational Neuroscience

Background:

  • Conventional diagnostic methods for brain tumors have limitations including low resolution, radiation exposure, and poor contrast.
  • Magnetic Resonance Imaging (MRI) offers high-resolution, non-invasive visualization crucial for accurate tumor characterization.
  • Advanced feature representation and machine learning algorithms are needed to enhance diagnostic capabilities.

Purpose of the Study:

  • To investigate the synergistic performance of multiple feature representation schemes and learning algorithms for MRI-based brain tumor identification.
  • To evaluate the efficacy of local Binary Patterns (LBP) in conjunction with classifiers like Support Vector Classifier (SVC) and Convolutional Neural Networks (CNN).
  • To assess the generalization power of proposed models using both small and large benchmark datasets.

Main Methods:

  • Exploration of feature representation schemes: local Binary Patterns (LBP), Gabor filters, Discrete Wavelet Transform, Fast Fourier Transform, and Gray-Level Run Length Matrix.
  • Application of learning algorithms: k-nearest Neighbor, Random Forest, Support Vector Classifier (SVC), probabilistic neural network (PNN), and Convolutional Neural Networks (CNN).
  • Statistical analysis (chi-square, p-value) and SHAP analysis were employed to validate feature importance and classification impact.

Main Results:

  • Local Binary Patterns (LBP) combined with Support Vector Classifier (SVC) and Convolutional Neural Networks (CNN) demonstrated high specificity and accuracy in initial tests.
  • On a small dataset, SVC achieved 98.06% accuracy and CNN achieved 97.8% accuracy.
  • On a large benchmark dataset, CNN yielded the highest accuracy at 98.9%, followed by SVC at 96.7%, indicating strong generalization.

Conclusions:

  • The combination of MRI-based feature extraction, particularly LBP, with advanced algorithms like CNN offers a highly accurate and automated approach to brain tumor diagnosis.
  • Convolutional Neural Networks (CNN) exhibit superiority in medical imaging due to their ability to learn intricate spatial patterns and generalize effectively.
  • This integrated approach enhances the accuracy, speed, and consistency of brain tumor detection, potentially improving patient outcomes and healthcare efficiency.