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

Survival Tree

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...

You might also read

Related Articles

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

Sort by

Same author

Modal analysis and design optimization of natural fibre-based sandwich structures for enhanced damping and stiffness.

Scientific reports·2025

Same author

Bald eagle-optimized transformer networks with temporal-spatial mid-level features for pancreatic tumor classification.

Biomedical physics & engineering express·2025

Same author

Optimizing pulmonary chest x-ray classification with stacked feature ensemble and swin transformer integration.

Biomedical physics & engineering express·2024

Same author

Optimisation ofLevilactobacillus brevis-fermented finger millet (Eleusine coracana) and evaluation of its effects on cancer cells (HCT116 and MDA-MB-231).

Methods (San Diego, Calif.)·2024

Same author

Computational Intelligence for Observation and Monitoring: A Case Study of Imbalanced Hyperspectral Image Data Classification.

Computational intelligence and neuroscience·2022

Same author

Hyperspectral Image Classification: Potentials, Challenges, and Future Directions.

Computational intelligence and neuroscience·2022

Search research articles

Related Experiment Video

Updated: May 7, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Optimizing stroke detection with genetic algorithm-based feature selection in deep learning models.

Gouri Sankar Nayak^1,2, Pradeep Kumar Mallick², Dhaneshwar Prasad Sahu¹

¹Department of Artificial Intelligence and Data Science, Vignan's Institute of Information Technology (VIIT), Visakhapatnam, India.

Applied Neuropsychology. Adult

|June 14, 2025

Summary

This summary is machine-generated.

Genetic Algorithms enhance deep learning models for improved brain stroke detection from neuroimaging. MobileNetV2 with GA achieved 97.21% accuracy, offering efficient, real-time clinical deployment potential.

Keywords:

Brain CT imaging deep learning feature selection genetic algorithm stroke detection

More Related Videos

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Related Experiment Videos

Last Updated: May 7, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Area of Science:

Medical Imaging Analysis
Artificial Intelligence in Healthcare
Computational Neuroscience

Background:

Brain stroke is a major global cause of death and disability.
Accurate and efficient diagnostic models are crucial for timely intervention.
Current diagnostic methods can be time-consuming or lack precision.

Purpose of the Study:

To enhance stroke detection accuracy using deep learning models.
To investigate the efficacy of Genetic Algorithm (GA)-based feature selection.
To compare the performance of InceptionV3, VGG19, and MobileNetV2 for stroke classification.

Main Methods:

Neuroimaging data classified as 'Normal' or 'Stroke' was utilized.
Genetic Algorithm (GA) was employed for optimizing feature selection.
Selected features were input into InceptionV3, VGG19, and MobileNetV2 deep learning models.

Main Results:

GA integration improved classification accuracy and reduced computational complexity.
MobileNetV2 achieved the highest accuracy at 97.21%.
Precision, recall, F1-score, and ROC curves confirmed model efficacy.

Conclusions:

GA-driven feature selection significantly enhances deep learning-based medical image classification for stroke.
MobileNetV2, optimized with GA, shows promise for real-time clinical stroke diagnosis due to its efficiency.
This approach offers a novel and effective strategy for reliable stroke detection in emergency settings.