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

You might also read

Related Articles

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

Sort by
Same author

Identification of Potential Biomarkers in Prostate Cancer Microarray Gene Expression Leveraging Explainable Machine Learning Classifiers.

Cancers·2025
Same author

Eye-XAI: an explainable artificial intelligence approach for eye disease detection using symptom analysis.

BMC medical informatics and decision making·2025
Same author

Integrating Multi-Omics and Medical Imaging in Artificial Intelligence-Based Cancer Research: An Umbrella Review of Fusion Strategies and Applications.

Cancers·2025
Same author

Lightweight Deep Learning Models with Explainable AI for Early Alzheimer's Detection from Standard MRI Scans.

Diagnostics (Basel, Switzerland)·2025
Same author

Leveraging Machine Learning for Severity Level-Wise Biomarker Identification in Prostate Cancer Microarray Gene Expression Data.

Biomedicines·2025
Same author

Automated lunar age detection through determining the day of the synodic month using convolutional neural networks.

Scientific reports·2025

Related Experiment Video

Updated: Aug 2, 2025

Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning
08:41

Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning

Published on: July 14, 2020

8.6K

Brain tumor detection and segmentation: Interactive framework with a visual interface and feedback facility for

Kashfia Sailunaz1, Deniz Bestepe2, Sleiman Alhajj3

  • 1Department of Computer Science, University of Calgary, Alberta, Canada.

Plos One
|April 17, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an automated system for detecting and segmenting brain tumors from MRI scans with over 90% accuracy. The web application aids early diagnosis, improving patient outcomes for malignant brain tumors.

More Related Videos

Multicolor 3D Printing of Complex Intracranial Tumors in Neurosurgery
14:15

Multicolor 3D Printing of Complex Intracranial Tumors in Neurosurgery

Published on: January 11, 2020

7.2K
A High-Throughput Image-Guided Stereotactic Neuronavigation and Focused Ultrasound System for Blood-Brain Barrier Opening in Rodents
08:02

A High-Throughput Image-Guided Stereotactic Neuronavigation and Focused Ultrasound System for Blood-Brain Barrier Opening in Rodents

Published on: July 16, 2020

4.9K

Related Experiment Videos

Last Updated: Aug 2, 2025

Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning
08:41

Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning

Published on: July 14, 2020

8.6K
Multicolor 3D Printing of Complex Intracranial Tumors in Neurosurgery
14:15

Multicolor 3D Printing of Complex Intracranial Tumors in Neurosurgery

Published on: January 11, 2020

7.2K
A High-Throughput Image-Guided Stereotactic Neuronavigation and Focused Ultrasound System for Blood-Brain Barrier Opening in Rodents
08:02

A High-Throughput Image-Guided Stereotactic Neuronavigation and Focused Ultrasound System for Blood-Brain Barrier Opening in Rodents

Published on: July 16, 2020

4.9K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Oncology
  • Neurosurgery

Background:

  • Malignant brain tumors are highly fatal due to challenges in early detection.
  • Current diagnostic methods include invasive and non-invasive techniques, with medical imaging being key.
  • Magnetic Resonance Imaging (MRI) is a primary non-invasive tool for brain tumor assessment.

Purpose of the Study:

  • To develop an automated system for detecting and segmenting brain tumors from 2D and 3D brain MRIs.
  • To create a user-friendly web application for accessing and analyzing brain MRI data.
  • To improve the accuracy and efficiency of brain tumor diagnosis through advanced AI models.

Main Methods:

  • Utilized deep neural networks, including Convolutional Neural Networks (CNN), U-Net, and U-Net++.
  • Developed a web application interface for user interaction and data input.
  • Implemented automated detection and segmentation algorithms for brain tumors in MRI scans.

Main Results:

  • Achieved over 90% accuracy in brain tumor detection and segmentation.
  • Demonstrated high performance with Dice scores exceeding 90%.
  • The system allows users to upload MRIs or access hospital databases for analysis.

Conclusions:

  • The automated web application system effectively detects and segments brain tumors from MRIs.
  • The system's high accuracy and user-friendly interface support early diagnosis and treatment planning.
  • Incorporating healthcare professional feedback enhances model training for future improvements.