Search research articles

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

Related Concept Videos

Magnetic Resonance Imaging

Magnetic Resonance Imaging

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...

You might also read

Related Articles

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

Sort by

Same author

A unified machine learning framework for intelligent resource allocation toward 6G wireless communications.

Scientific reports·2026

Same author

BEML-sonar: a bio-inspired echolocation and machine learning-enhanced SONAR for underwater object detection and navigation.

Scientific reports·2026

Same author

Synthesis and characterization of a novel phosphatidylinositol 5-phosphate (PI(5)P) photoaffinity probe.

RSC chemical biology·2026

Same author

Cardiovascular adverse events associated with bispecific antibodies in relapsed or refractory B-cell non-Hodgkin lymphomas.

Journal of hematology & oncology·2026

Same author

Towards precision agriculture for assessing germination rates and density of rice seedling using hierarchical convolutional neural network on drone imagery.

Scientific reports·2026

Same author

Demographics and clinical implications of a global bibliographic profile for the hypertrophic cardiomyopathy literature over 66 years (1960-2025).

International journal of cardiology·2026

Search research articles

Related Experiment Video

Updated: Jun 4, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Leveraging transfer learning-driven convolutional neural network-based semantic segmentation model for medical image

Amal Alshardan¹, Nuha Alruwais², Hamed Alqahtani³

¹Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia. amalshardan@pnu.edu.sa.

Scientific Reports

|December 18, 2024

Summary

This study introduces a new Deep Transfer Learning with Semantic Segmentation based Medical Image Analysis (DTLSS-MIA) technique for accurate brain tumor (BT) segmentation in MRI scans. The DTLSS-MIA method achieves 99.53% accuracy, improving early diagnosis and treatment planning.

Keywords:

Brain tumor Crayfish optimization DeepLabv3+MRI image Medical image Semantic segmentation

More Related Videos

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

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

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

Published on: January 7, 2019

Related Experiment Videos

Last Updated: Jun 4, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

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

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

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

Published on: January 7, 2019

Area of Science:

Medical Image Analysis
Artificial Intelligence in Healthcare
Radiology

Background:

Brain tumor (BT) segmentation from Magnetic Resonance Images (MRI) is crucial for timely diagnosis and treatment but remains challenging due to tissue complexities.
Manual segmentation by radiologists is tedious and prone to errors.
Existing deep learning (DL) models, particularly convolutional networks, face challenges with information loss and parameter complexity in encoding-decoding processes.

Purpose of the Study:

To present a novel Deep Transfer Learning with Semantic Segmentation based Medical Image Analysis (DTLSS-MIA) technique for precise brain tumor segmentation in MRI.
To address the limitations of existing deep convolutional networks in handling information loss and parameter complexity.

Main Methods:

The DTLSS-MIA technique employs Median filtering (MF) for MRI image quality optimization and noise reduction.
DeepLabv3+ with an EfficientNet backbone is utilized for semantic segmentation to identify affected brain regions.
Capsule Network (CapsNet) architecture is used for feature extraction.
Crayfish Optimization (CFO) algorithm tunes hyperparameters of a diffusion variational autoencoder (D-VAE) for classification.

Main Results:

The DTLSS-MIA technique demonstrated superior performance in segmenting brain tumor areas from MRI scans.
The method achieved a high accuracy of 99.53% on a benchmark dataset.
Simulation analysis confirmed the effectiveness of the DTLSS-MIA technique compared to other existing methods.

Conclusions:

The proposed DTLSS-MIA technique offers an effective and accurate solution for brain tumor segmentation using MRI.
This advancement can significantly aid radiologists in early diagnosis and treatment planning, potentially saving lives.
The integration of deep transfer learning, semantic segmentation, and optimization algorithms shows promise for medical image analysis.