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

You might also read

Related Articles

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

Sort by

Same author

Loop parallelization in source code for internet of things computing using hybrid heuristic algorithm.

PloS one·2026

Same author

ReFaceX: donor-driven reversible face anonymisation with detached recovery.

Scientific reports·2026

Same author

Editorial: Advancing knowledge-based economies and societies through AI and optimization: innovations, challenges, and implications.

Frontiers in artificial intelligence·2026

Same author

A dynamic decision support system to tackle the spread of COVID-19: predictive and clustering approaches.

Informatics for health & social care·2026

Same author

A robust bilevel decision-making model for earthquake disaster waste management.

Waste management (New York, N.Y.)·2025

Same author

The role of data augmentation and attention mechanisms in UNet and ConvNeXt architectures for optimizing breast tumor segmentation.

Scientific reports·2025

Same journal

ECG arrhythmia classification via wavelet-driven feature extraction and swarm-optimised gradient boosting.

Computers in biology and medicine·2026

Same journal

Electro-osmotic metachronal cilia transport of viscoelastic blood infused with penta-hybrid nanoparticles in an oviduct: Analytical and neural network modeling.

Computers in biology and medicine·2026

Same journal

sEEGnal: an automated EEG preprocessing pipeline evaluated against expert-driven preprocessing.

Computers in biology and medicine·2026

Same journal

Corrigendum to "Integrating experimental biology, computational methods, and artificial Intelligence in anticancer drug discovery: Bridging the translational Gap" [Comput. Biol. Med. 213 (2026) 111832].

Computers in biology and medicine·2026

Same journal

Organ dose optimization for a point-of-care forearm X-ray photon-counting CT.

Computers in biology and medicine·2026

Same journal

Physics-guided transformation of breathomic feature spaces into disease-specific representations for respiratory disease classification.

Computers in biology and medicine·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 10, 2025

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

Brain tumor segmentation based on optimized convolutional neural network and improved chimp optimization algorithm.

Ramin Ranjbarzadeh¹, Payam Zarbakhsh², Annalina Caputo¹

¹School of Computing, Faculty of Engineering and Computing, Dublin City University, Ireland.

Computers in Biology and Medicine

|November 24, 2023

Summary

This summary is machine-generated.

This study introduces an optimized Convolutional Neural Network (CNN) for brain tumor segmentation using multiple Magnetic Resonance Imaging (MRI) sequences. The framework achieves high accuracy in segmenting tumors, improving diagnosis and treatment planning.

Keywords:

Brain tumor Convolutional neural network Deep learning Feature selection Improved chimp optimization algorithm

More Related Videos

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

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

Related Experiment Videos

Last Updated: Jul 10, 2025

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

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

Area of Science:

Medical Imaging
Artificial Intelligence
Neuro-oncology

Background:

Accurate brain tumor segmentation is crucial for diagnosis and treatment planning.
Magnetic Resonance Imaging (MRI) with multiple sequences (T1, Flair, T1ce, T2) provides diverse information for tumor identification.
Challenges exist in segmenting fuzzy tumor borders due to variations in image illumination.

Purpose of the Study:

To develop an automatic and robust brain tumor segmentation framework.
To enhance segmentation accuracy by utilizing multiple MRI modalities.
To optimize a Convolutional Neural Network (CNN) using an Improved Chimp Optimization Algorithm (IChOA).

Main Methods:

Utilized four MRI sequence images (T1, Flair, T1ce, T2) as input.
Employed an Improved Chimp Optimization Algorithm (IChOA) for feature selection and CNN hyperparameter optimization.
Integrated a Support Vector Machine (SVM) classifier for initial feature selection.

Main Results:

Achieved superior performance on the BRATS 2018 dataset.
Reported high segmentation accuracy with Precision of 97.41%, Recall of 95.78%, and Dice Score of 97.04%.
Demonstrated improved results compared to existing brain tumor segmentation frameworks.

Conclusions:

The proposed IChOA-optimized CNN framework offers a robust solution for automatic brain tumor segmentation.
The integration of multiple MRI modalities and advanced optimization techniques enhances segmentation accuracy.
This approach holds significant potential for improving clinical diagnosis and treatment planning for brain tumors.