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

Bioinformatics Approaches for Functional and Structural Annotation and Molecular Docking Study of a Hypothetical Protein From Staphylococcus aureus.

BioMed research international·2026
Same author

Rhamnogalacturonan-II Dimerisation Reinforces Salt Resistance in Sugar Beet.

Plant, cell & environment·2026
Same author

High-resolution gridded streamflow data for Ganges-Brahmaputra-Meghna River Basins in Bangladesh (1951-2023).

Scientific data·2025
Same author

ContourTL-Net: Contour-Based Transfer Learning Algorithm for Early-Stage Brain Tumor Detection.

International journal of biomedical imaging·2024
Same author

Analysis and identification of genomic and immunogenic features of dengue serotype 3 genomes obtained during the 2019 outbreak in Bangladesh.

New microbes and new infections·2022
Same author

A review on machine learning and deep learning for various antenna design applications.

Heliyon·2022
Same journal

Deep Learning for Brain Tumour Analysis: A Systematic Review of CNN-Transformer Hybrids in Multimodal Imaging.

International journal of biomedical imaging·2026
Same journal

Brain Tumor Segmentation Using U-Net With ResNet50 Encoder for Enhanced MRI Analysis.

International journal of biomedical imaging·2026
Same journal

Generative AI-Driven CNN Framework for Enhanced Lung Cancer Detection, Prediction, and Treatment: A Novel Approach to Overcoming AI Limitations.

International journal of biomedical imaging·2026
Same journal

Enhancing the Generalizability of Deep Learning-Based Models for Lung Field Segmentation in Chest Radiographs Using Edge-Assisted Multiscale Feature Fusion.

International journal of biomedical imaging·2026
Same journal

Personalized PET Imaging in Gastric Cancer: An Umbrella Review of Meta-Analyses to Guide Radiopharmaceutical Selection and Clinical Indication.

International journal of biomedical imaging·2026
Same journal

Clinician-Centric Explainable Artificial Intelligence Framework for Medical Imaging Diagnostics: A Systematic Review.

International journal of biomedical imaging·2026
See all related articles

Related Experiment Video

Updated: Sep 11, 2025

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.3K

Enhanced Brain Tumor Segmentation Using CBAM-Integrated Deep Learning and Area Quantification.

Rafiqul Islam1, Sazzad Hossain1

  • 1Department of Computer Science and Engineering, Dhaka University of Engineering & Technology, Gazipur, Bangladesh.

International Journal of Biomedical Imaging
|August 11, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an improved deep learning model for brain tumor segmentation in MRI scans. The enhanced U-Net architecture with a convolutional block attention module (CBAM) achieves higher accuracy and efficiency in delineating tumors and measuring their extent.

Keywords:
area quantificationbrain tumor analysislightweight U-Net modelmagnetic resonance imagingsegmentation

More Related Videos

Live Imaging of Microtubule Dynamics in Glioblastoma Cells Invading the Zebrafish Brain
09:29

Live Imaging of Microtubule Dynamics in Glioblastoma Cells Invading the Zebrafish Brain

Published on: July 29, 2022

2.8K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

2.9K

Related Experiment Videos

Last Updated: Sep 11, 2025

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.3K
Live Imaging of Microtubule Dynamics in Glioblastoma Cells Invading the Zebrafish Brain
09:29

Live Imaging of Microtubule Dynamics in Glioblastoma Cells Invading the Zebrafish Brain

Published on: July 29, 2022

2.8K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

2.9K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neuro-oncology

Background:

  • Accurate brain tumor segmentation from MRI is crucial for diagnosis and treatment but challenging due to tumor complexity and manual segmentation limitations.
  • Automated methods are needed to improve efficiency and reduce human error in delineating brain tumor boundaries and quantifying tumor burden.

Purpose of the Study:

  • To enhance the accuracy and efficiency of MRI-based brain tumor segmentation using a novel deep learning approach.
  • To develop a comprehensive framework for both precise tumor segmentation and quantitative tumor area measurement.

Main Methods:

  • Integration of a convolutional block attention module (CBAM) into a VGG19-based U-Net architecture.
  • Utilization of depthwise and pointwise convolutions for improved feature extraction and processing efficiency.
  • Development of a new method for computing tumor area based on segmented pixels for quantitative analysis.

Main Results:

  • The proposed deep learning framework demonstrates enhanced precision in brain tumor segmentation.
  • The model effectively incorporates tumor area measurement, providing quantifiable data for clinical interpretation.
  • Qualitative assessments confirm the model's accuracy and dependability in segmenting tumor masks.

Conclusions:

  • The developed methodology offers improved segmentation accuracy, efficiency, and clinical relevance for brain tumor analysis.
  • This approach has the potential to enhance early-stage tumor diagnosis, treatment planning, and patient monitoring.
  • The integration of CBAM and advanced convolutions represents a significant advancement in automated neuro-imaging analysis.