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 Videos

RepSE-CBAMNet: A Hybrid Attention-Enhanced CNN for Brain Tumor Detection.

Farhan Khan1, Gerasimos Katsagannis1, Sandeep Singh Sengar1

  • 1School of Technologies, Cardiff Metropolitan University, United Kingdom.

Studies in Health Technology and Informatics
|May 17, 2025
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

Endovascular Therapy for Acute Ischemic Stroke: Current Evidence and Evolving Practices.

Current cardiology reports·2026
Same author

Development and External Validation of an Electronic Health Record-Derived Model for 30-Day Periprocedural Ischemic Stroke.

Journal of the American Heart Association·2026
Same author

Safety of Endovascular Thrombectomy in Isolated Cervical Internal Carotid Artery Occlusion While on Oral Anticoagulation.

Journal of stroke·2026
Same author

YOLO-LXR: An Enhanced Model for Pathology Detection in Chest X-Rays.

Studies in health technology and informatics·2026
Same author

Global and regional level association between depression and glycemic control in type 2 diabetes mellitus: A systematic review and meta-analysis.

Indian journal of psychiatry·2026
Same author

Possible Diagnostic Error in Cervical Artery Dissection: Analysis of STOP-CAD Study.

Journal of the American Heart Association·2026
Same journal

A GenAI Pipeline for Violinist Kinematic Data Management.

Studies in health technology and informatics·2026
Same journal

AMAL-For-Qatar: A Comprehensive AI Ecosystem for Fetal Ultrasound Analysis - Project Overview and Achievements.

Studies in health technology and informatics·2026
Same journal

Longitudinal Treatment-Aware Multimodal AI for Dermatology: A Scoping Review.

Studies in health technology and informatics·2026
Same journal

Predicting Postpartum Depression Using Imbalance-Aware Machine Learning.

Studies in health technology and informatics·2026
Same journal

Validation of Deep-Learning Models for Autosegmentation of Brain Metastases.

Studies in health technology and informatics·2026
Same journal

Delay-Dependent Gating in Modular RNNs.

Studies in health technology and informatics·2026
See all related articles

This study introduces an enhanced YOLO architecture for improved brain tumor detection. The novel model integrates attention mechanisms and residual blocks, boosting diagnostic accuracy for better patient outcomes.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Effective brain tumor detection is crucial for timely diagnosis and treatment, directly impacting patient survival rates and quality of life.
  • Current detection methods face challenges in accurately identifying complex tumor features in medical images.
  • The integration of advanced deep learning architectures is essential for enhancing diagnostic precision.

Purpose of the Study:

  • To present an enhanced YOLO (You Look Only Once) architecture specifically designed for improved brain tumor detection.
  • To significantly improve detection accuracy by incorporating Convolutional Block Attention Modules (CBAMs), Squeeze-and-Excitation Blocks (SE), and Residual Blocks.
  • To provide a robust and efficient tool for supporting clinical decision-making in neuro-oncology.
Keywords:
Convolutional Block Attention ModuleGeneralized Efficient Layer Aggregation NetworkResidual BlockSqueeze-and-excitation Networks

Related Experiment Videos

Main Methods:

  • Developed an enhanced YOLO architecture integrating CBAMs, SE blocks, and Residual Blocks for feature extraction and attention.
  • CBAMs were utilized to focus on critical spatial and channel-wise features in medical images.
  • SE blocks were employed to enhance feature representation by emphasizing important channels, while Residual blocks addressed the vanishing gradient problem.

Main Results:

  • The enhanced model demonstrated significant improvements in precision and AP50:95 on one brain tumor dataset.
  • The architecture showed competitive performance when compared against established models like RepVGG-GELAN, YOLOv9c, RCS-YOLO, and Yolov5L.
  • The integration of attention mechanisms and efficient blocks led to superior feature learning and detection capabilities.

Conclusions:

  • The proposed enhanced YOLO architecture offers a promising advancement for brain tumor detection.
  • The model's improved accuracy supports more reliable diagnoses and has the potential to enhance clinical decision-making.
  • Publicly available code facilitates further research and application in medical image analysis.