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

Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

7.1K
Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
7.1K

You might also read

Related Articles

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

Sort by
Same author

Retraction Note: An efficient hybrid stock trend prediction system during COVID-19 pandemic based on stacked-LSTM and news sentiment analysis.

Multimedia tools and applications·2026
Same author

A cancelable ear recognition system via optimized deep feature fusion.

Scientific reports·2026
Same author

Dynamic driver drowsiness detection with attention enhanced convolutional neural networks for real time monitoring and road safety applications.

Scientific reports·2026
Same author

An automated cloud-based system for in-situ geotechnical site characterization using cone penetration test (CPT/CPTu).

Scientific reports·2026
Same author

A hybrid transformer-zero-shot learning framework with Muon optimization for intelligent channel estimation in MIMO wireless systems.

Scientific reports·2026
Same author

Efficient EOG-based movement classification in IoMT using machine learning algorithms for people with motor disabilities.

Disability and rehabilitation. Assistive technology·2026

Related Experiment Video

Updated: Aug 3, 2025

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.5K

Simultaneous Super-Resolution and Classification of Lung Disease Scans.

Heba M Emara1, Mohamed R Shoaib2, Walid El-Shafai3,4

  • 1Department of Electronics and Communications Engineering, High Institute of Electronic Engineering, Ministry of Higher Education, Bilbis-Sharqiya 44621, Egypt.

Diagnostics (Basel, Switzerland)
|April 13, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an AI system using super-resolution and deep learning for diagnosing lower respiratory infections from chest X-rays and CT scans. The advanced computer-aided diagnostic system achieved 98.028% accuracy, aiding early detection in resource-limited settings.

Keywords:
Coronaviruschest X-ray radiographsconvolutional neural networkimage super-resolutionmulti-class SVM

More Related Videos

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

561
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

14.2K

Related Experiment Videos

Last Updated: Aug 3, 2025

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.5K
Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

561
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

14.2K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Pulmonology

Background:

  • Acute lower respiratory infections are a major cause of mortality, especially in developing nations.
  • Current diagnostic methods require improvement, particularly in resource-limited environments.
  • Artificial intelligence (AI) applications in analyzing chest X-rays and computed tomography (CT) for these infections are underexplored.

Purpose of the Study:

  • To develop and evaluate a computer-aided diagnostic (CAD) system for detecting common pulmonary diseases from chest X-ray and CT images.
  • To enhance image quality using super-resolution (SR) techniques for improved diagnostic accuracy.
  • To leverage deep learning (DL) for both image reconstruction and disease classification.

Main Methods:

  • The proposed system integrates super-resolution (SR) techniques with deep learning (DL) models.
  • The InceptionResNetv2 model was employed as a feature extractor, coupled with a multi-class support vector machine (MCSVM) classifier.
  • Performance was benchmarked against other models like Resnet101 and Inceptionv3, and classifier effectiveness (softmax vs. MCSVM) was assessed on public datasets.

Main Results:

  • The developed system achieved a high classification accuracy of 98.028%.
  • The combination of SR techniques and the InceptionResNetv2 model demonstrated superior performance.
  • The system proved effective in classifying various pulmonary conditions including COVID-19, pneumonia, tuberculosis, lung opacity, and carcinoma.

Conclusions:

  • The AI-powered system shows significant potential as a screening tool for lower respiratory disorders.
  • It can assist clinicians in interpreting complex chest imaging data, enhancing diagnostic capabilities.
  • The system offers valuable diagnostic support, especially in resource-limited healthcare settings.