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

5.2K
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...
5.2K

You might also read

Related Articles

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

Sort by
Same author

Next-generation COVID-19 detection using a metasurface biosensor with machine learning-enhanced refractive index sensing.

Scientific reports·2025
Same author

Human lung cancer classification and comprehensive analysis using different machine learning techniques.

Microscopy research and technique·2024
Same author

Band power feature part-based convolutional neural network with African vulture optimization fostered channel selection for EEG classification.

Computer methods in biomechanics and biomedical engineering·2024
Same author

M2AI-CVD: Multi-modal AI approach cardiovascular risk prediction system using fundus images.

Network (Bristol, England)·2024
Same author

Impact of Bonding Temperature on Microstructure, Mechanical, and Fracture Behaviors of TLP Bonded Joints of Al2219 with a Cu Interlayer.

ACS omega·2023
Same author

Deep Transfer Learning Technique for Multimodal Disease Classification in Plant Images.

Contrast media & molecular imaging·2023

Related Experiment Video

Updated: Jul 5, 2025

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

Evolutionary gravitational neocognitron neural network optimized with marine predators optimization algorithm for MRI

A Lakshmi1, Manjunathan Alagarsamy2, A Anbarasa Pandian3

  • 1Department of Electronics and Communication Engineering, Ramco Institute of Technology, Rajapalayam, Tamil Nadu, India.

Electromagnetic Biology and Medicine
|January 13, 2024
PubMed
Summary

This study introduces a novel method for brain tumor classification using Magnetic Resonance Imaging (MRI). The proposed Evolutionary Gravitational Neocognitron Neural Network with Marine Predators Algorithm (EGNNN-MPA) significantly improves accuracy in detecting brain tumors.

Keywords:
Evolutionary Gravitational Neocognitron Neural Network (EGNNN)Marine Predators Algorithm (MPA)Savitzky-Golay Denoising methodVisual geometry group network (VGG16)

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 Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.6K

Related Experiment Videos

Last Updated: Jul 5, 2025

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
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 Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.6K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Neuroscience

Background:

  • Magnetic Resonance Imaging (MRI) is crucial for human brain tumor diagnosis.
  • Existing brain tumor detection methods often lack accuracy and efficiency.
  • Accurate classification of tumor types (meningioma, glioma, pituitary) and normal tissue is challenging.

Purpose of the Study:

  • To propose an advanced method for Magnetic Resonance Imaging (MRI) Brain Tumor Classification (BTC).
  • To enhance the accuracy and reduce computation time in brain tumor detection.
  • To introduce the Evolutionary Gravitational Neocognitron Neural Network optimized with Marine Predators Algorithm (EGNNN-MPA) for MRI-BTC.

Main Methods:

  • Utilized the Brats MRI image dataset for brain tumor images.
  • Pre-processed images using the Savitzky-Golay Denoising approach.
  • Extracted features (Grey level, Haralick Texture) using Visual Geometry Group network (VGG16).
  • Employed EGNNN classifier with VGG16 integration and optimized weights using Marine Predators Optimization Algorithm (MPA).

Main Results:

  • The EGNNN-VGG16-MPA-MRI-BTC method demonstrated superior performance.
  • Achieved significant improvements in accuracy, precision, and sensitivity compared to existing models (AlexNet-SVM, RESNET-SGD, MobileNet-V2).
  • Specific accuracy gains of 38.98%, 46.74%, 23.27% were noted against comparative methods.

Conclusions:

  • The proposed EGNNN-VGG16-MPA-MRI-BTC model offers a highly accurate and efficient solution for brain tumor classification from MRI scans.
  • This approach addresses limitations of previous methods in terms of accuracy and computational cost.
  • The study highlights the potential of integrating advanced neural networks and optimization algorithms in medical image analysis.