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

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

5.0K
Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
5.0K
Brain Imaging01:14

Brain Imaging

219
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...
219

You might also read

Related Articles

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

Sort by
Same author

PI-HydroGNN: a physics-informed spatiotemporal graph neural network framework for hydraulic reliability, leakage detection, and energy-efficient operation in water distribution systems.

Scientific reports·2026
Same author

Progression-guided spatiotemporal memory transformers for accurate and consistent longitudinal brain tumor segmentation.

Scientific reports·2026
Same author

Temporally consistent longitudinal brain tumor segmentation using a temporal spatial transformer network.

Scientific reports·2026
Same author

Adaptive fuzzy deep learning with multimodal sensor fusion for enhanced plant disease detection.

Scientific reports·2026
Same author

GNN-ML-FRL: a graph-enhanced meta-adaptive federated learning framework for scalable pest identification and ernvironmental modeling.

Scientific reports·2026
Same author

An advanced hybrid deep learning framework for high-precision brain tumor detection and classification in MRI scans.

Scientific reports·2026

Related Experiment Video

Updated: Jun 15, 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

40.2K

Automated brain tumor diagnostics: Empowering neuro-oncology with deep learning-based MRI image analysis.

Subathra Gunasekaran1, Prabin Selvestar Mercy Bai2, Sandeep Kumar Mathivanan3

  • 1Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, India.

Plos One
|August 27, 2024
PubMed
Summary

This study introduces a hybrid deep learning model, ConvNet-ResNeXt101, for accurate brain tumor segmentation and classification from MRI scans. The novel approach achieves high accuracy, improving early detection and treatment planning for brain tumors.

More Related Videos

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging
06:44

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging

Published on: June 7, 2020

7.3K
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

8.9K

Related Experiment Videos

Last Updated: Jun 15, 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

40.2K
Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging
06:44

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging

Published on: June 7, 2020

7.3K
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

8.9K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Brain tumors present a significant health challenge, necessitating early detection for effective treatment.
  • Magnetic Resonance Imaging (MRI) is crucial for brain tumor diagnosis, but accurate segmentation is difficult due to tumor complexity.
  • Precise tumor segmentation is vital for treatment planning and prognosis.

Purpose of the Study:

  • To develop a novel hybrid deep learning technique for automated brain tumor segmentation and classification.
  • To improve the accuracy and efficiency of brain tumor analysis using MRI data.

Main Methods:

  • Utilized the BRATS 2020 dataset for MRI images and tumor segmentations.
  • Employed batch normalization and AlexNet for feature extraction.
  • Applied Advanced Whale Optimization (AWO) for optimal feature selection.
  • Implemented a hybrid ConvNet-ResNeXt101 model for segmentation and classification.

Main Results:

  • The ConvNet-ResNeXt101 model achieved 99.27% accuracy for tumor core segmentation.
  • Demonstrated superior performance compared to existing methods.
  • Achieved a minimum learning elapsed time of 0.53 seconds.

Conclusions:

  • The proposed ConvNet-ResNeXt101 hybrid deep learning model offers a highly accurate and efficient solution for brain tumor segmentation and classification.
  • This technique has the potential to significantly enhance early brain tumor detection and treatment planning.