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

Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

5.6K
The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
5.6K

You might also read

Related Articles

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

Sort by
Same author

Learning continuous activation fields from microscopic SEM images of lycoperdioid fungi via CNN-guided neural operator modeling.

Scientific reports·2026
Same author

Integrating scanning electron microscopy, explainable deep learning, and ITS sequencing for accurate identification in some species Geastrum.

Scientific reports·2026
Same author

A comparative analysis of single- and dual-backbone deep learning architectures with explainable AI for cherry leaf disease classification.

Scientific reports·2026
Same author

Hybrid Graph-Machine Learning Framework for Accurate and Interpretable Band Gap Prediction.

Journal of chemical information and modeling·2026
Same author

Reply to Pastore, E.P. Comment on "Korkmaz et al. A Deep Learning and Explainable AI-Based Approach for the Classification of Discomycetes Species. <i>Biology</i> 2025, <i>14</i>, 719".

Biology·2026
Same author

Combination Ensemble and Explainable Deep Learning Framework for High-Accuracy Classification of Wild Edible Macrofungi.

Biology·2025
Same journal

Correction: Luca et al. Global and Regional Diagnostic Results of Progress Toward Cervical Cancer Elimination, According to the WHO Strategy: A Systematic Literature Review with Narrative Synthesis. <i>Diagnostics</i> 2026, <i>16</i>, 1224.

Diagnostics (Basel, Switzerland)·2026
Same journal

Association Between Systemic Inflammatory Response Biomarkers and Disease Activity in Systemic Lupus Erythematosus: A Multi-Center Retrospective Study.

Diagnostics (Basel, Switzerland)·2026
Same journal

Vertebrogenic Low Back Pain and Basivertebral Nerve Ablation: A Review of Mechanisms, Imaging-Driven Selection, and Clinical Outcomes.

Diagnostics (Basel, Switzerland)·2026
Same journal

Multivalvular Carcinoid Heart Disease: The Role of Echocardiography in Diagnosis and Selection for Heterotopic Bicaval Valve Implantation.

Diagnostics (Basel, Switzerland)·2026
Same journal

Data-Efficient and Explainable Multimodal Survival Prediction in NSCLC Using Deep Image Embeddings, Clinical Variables, and Gradient-Boosted Trees.

Diagnostics (Basel, Switzerland)·2026
Same journal

Anomalous Left Coronary Artery from the Pulmonary Artery: Cinematic Volume Rendering Technique for Enhanced Anatomic Visualization.

Diagnostics (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jun 19, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

3.9K

A Novel Hybrid Machine Learning-Based System Using Deep Learning Techniques and Meta-Heuristic Algorithms for Various

Yezi Ali Kadhim1,2,3, Mehmet Serdar Guzel4, Alok Mishra5,6

  • 1College of Engineering, University of Baghdad, Jadriyah, Baghdad 10071, Iraq.

Diagnostics (Basel, Switzerland)
|July 27, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a hybrid machine learning approach combining deep learning and meta-heuristic algorithms for precise medical image diagnosis. The innovative method achieved high accuracy in classifying brain tumors and COVID-19 cases from medical imaging data.

Keywords:
COVID-19autoencoderbrain tumorclassificationdeep learningmedical datasetmeta-heuristic algorithm

More Related Videos

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.4K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

Related Experiment Videos

Last Updated: Jun 19, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

3.9K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.4K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

Area of Science:

  • Computer Science in Medicine
  • Artificial Intelligence in Healthcare
  • Medical Image Analysis

Background:

  • Accurate and timely disease diagnosis is critical for improving patient outcomes.
  • Advancements in computer science, particularly machine learning, offer powerful tools for medical applications.
  • Existing methods for analyzing complex medical data like MRI and CXR images can be enhanced for better precision.

Purpose of the Study:

  • To develop and evaluate a hybrid machine learning model for accurate classification of medical images.
  • To combine deep learning techniques with meta-heuristic algorithms for feature selection and dimensionality reduction.
  • To validate the model's performance on diverse medical datasets, including brain tumors and COVID-19 chest X-rays.

Main Methods:

  • Utilized a hybrid machine learning approach integrating deep learning (Convolutional Neural Network - CNN, Autoencoder) with a meta-heuristic algorithm (Particle Swarm Optimization - PSO).
  • Developed a combination network to extract features using deep learning and select optimal features via PSO, reducing data dimensionality.
  • Applied the hybrid model to two medical datasets: brain tumor MRI and COVID-19 Chest X-rays (CXRs).

Main Results:

  • Achieved highly accurate classification results on both datasets, demonstrating the model's effectiveness.
  • The COVID-19 dataset yielded a maximum accuracy of 99.76% using a CNN-PSO-SVM combination.
  • The brain tumor dataset achieved a maximum accuracy of 99.51% with an Autoencoder-PSO-KNN combination.

Conclusions:

  • The proposed hybrid machine learning method offers an innovative and effective solution for medical image classification.
  • This approach significantly reduces data dimensionality while preserving performance, leading to highly accurate diagnostic predictions.
  • The model's success across different medical imaging tasks highlights its potential for widespread application in clinical settings.