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 Experiment Video

Updated: Jul 10, 2026

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

Deep learning based two-way feature depiction model for brain tumor detection.

Shabana Urooj1, Kiran Napte2, Najah Alsubaie3

  • 1Department of Electrical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Plos One
|July 2, 2026
PubMed
Summary
This summary is machine-generated.

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

A Novel Optimized Hybrid Deep Learning Framework for Mental Stress Detection Using Electroencephalography.

Brain sciences·2025
Same author

Optimization of Anthralin Microemulgel Targeted Delivery for Psoriasis and Acne.

Molecules (Basel, Switzerland)·2025
Same author

Investigation of Channel Mobility Enhancement Techniques Using Si/SiGe/GeSn Materials in Orthogonally Oriented Selective Buried Triple Gate Vertical Power MOSFET: Design and Performance Analysis.

Micromachines·2025
Same author

Design and Performance Assessment of Biocompatible Capacitive Pressure Sensors with Circular and Square Geometries Using ANSYS Workbench.

Sensors (Basel, Switzerland)·2025
Same author

Enhancing Diagnostic Decision-Making: Ensemble Learning Techniques for Reliable Stress Level Classification.

Diagnostics (Basel, Switzerland)·2023
Same author

Use of a Novel Deep Learning Open-Source Model for Quantification of Ki-67 in Breast Cancer Patients in Pakistan: A Comparative Study between the Manual and Automated Methods.

Diagnostics (Basel, Switzerland)·2023

This study introduces a novel deep learning model for brain glioma detection, significantly improving accuracy over traditional methods. The two-way feature depiction model (TWFDM) enhances early cancer diagnosis from MRI scans.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Brain tumors, particularly gliomas, are a leading cause of cancer mortality worldwide.
  • Current diagnostic methods involving tissue biopsy are invasive, time-consuming, and may yield delayed results.
  • Existing deep learning models for glioma detection face challenges like poor explainability, generalization, and class imbalance.

Purpose of the Study:

  • To develop an advanced deep learning model for accurate and efficient brain glioma detection.
  • To address the limitations of current deep learning approaches in terms of explainability and detection rates.
  • To introduce a novel two-way feature depiction model (TWFDM) for enhanced brain tumor classification.

Main Methods:

  • The proposed Two-Way Feature Depiction Model (TWFDM) integrates 2D-Deep Convolutional Neural Networks (DCNN) with 1D-DCNN.

Related Experiment Videos

Last Updated: Jul 10, 2026

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

  • Raw MRI images are processed by 2D-DCNN, while 1D-DCNN analyzes handcrafted features like Local Binary Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM), and Histogram of Oriented Gradient (HOG).
  • Improved Particle Swarm Optimization (IPSO) is employed for efficient feature selection, reducing computational complexity.

    • Main Results:

    • The TWFDM achieved a high overall accuracy of 96.25% for four-class brain tumor classification.
    • Excellent performance metrics were recorded, including a recall of 96.34%, precision of 96.31%, and F1-score of 96.32%.
    • The model demonstrated significant improvements compared to traditional diagnostic techniques and existing deep learning methods.

    Conclusions:

    • The TWFDM offers a highly accurate and efficient deep learning solution for brain glioma detection using MRI data.
    • This approach effectively combines image-based and feature-based deep learning, overcoming limitations of prior methods.
    • The proposed model represents a substantial advancement in automated brain tumor diagnosis, paving the way for improved patient outcomes.