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

Gallstone disease classification using SLOA-optimized CatBoost classifier with explainable AI.

PloS one·2026
Same author

Graph-guided adaptive companding for PAPR reduction in power-domain NOMA systems.

PloS one·2026
Same author

A residual-learning MMSE neural detector for 6G MIMO-OTFS systems under diverse channel conditions.

Scientific reports·2026
Same author

Deep ensemble of multi-head attention CNNs for histopathological image-based of lung and colon cancer diagnosis.

Digital health·2026
Same author

Quad-Element Implantable MIMO Antenna for Wireless Capsule Endoscopy.

Sensors (Basel, Switzerland)·2026
Same author

Feature reduction using swarm optimization and random forest classifiers for early diabetes risk prediction.

Scientific reports·2026

Related Experiment Video

Updated: Jun 6, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

678

Heterogeneous virus classification using a functional deep learning model based on transmission electron microscopy

Niloy Sikder1,2, Md Al-Masrur Khan3, Anupam Kumar Bairagi4

  • 1Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.

Scientific Reports
|November 23, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning model for rapid virus identification using Transmission Electron Microscopy images. The model accurately classifies 14 virus types, offering a fast and reliable diagnostic tool.

Keywords:
2D discrete cosine transformBiomedical image processingComputer-aided diagnosisFunctional CNN modelLocal standard deviation filteringTransmission electron microscopyVirus image classification

More Related Videos

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

1.3K

Related Experiment Videos

Last Updated: Jun 6, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

678
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

1.3K

Area of Science:

  • Virology
  • Computational Biology
  • Microscopy

Background:

  • Viruses are microscopic infectious agents with high adaptability and potential for severe complications.
  • Accurate and timely virus identification is crucial for managing biological threats to plants and animals.
  • Manual virus detection methods are often slow and lack precision.

Purpose of the Study:

  • To develop a computer-based automatic diagnosis method for instant virus identification.
  • To propose a deep learning-based classification model utilizing Transmission Electron Microscopy (TEM) images.
  • To enhance the speed and accuracy of virus type determination.

Main Methods:

  • Utilized a dataset of Transmission Electron Microscopy (TEM) images for virus classification.
  • Implemented two image processing techniques for noise reduction in raw microscopy images.
  • Developed and applied a Convolutional Neural Network (CNN) model for virus type classification.

Main Results:

  • The proposed deep learning model achieved a maximum classification accuracy of 97.44%.
  • The model demonstrated high reliability in differentiating among 14 distinct virus types.
  • Achieved a maximum F1-score of 97.44%, indicating strong performance.

Conclusions:

  • The deep learning-based approach offers an effective and reliable method for virus identification.
  • This automated scheme provides a fast and dependable complement to existing diagnostic procedures.
  • The model's high accuracy supports its implementation in clinical and research settings.