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

Classification of Illness01:17

Classification of Illness

7.5K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
7.5K
Classification of Systems-I01:26

Classification of Systems-I

183
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
183
Classification of Systems-II01:31

Classification of Systems-II

141
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
141
Aggregates Classification01:29

Aggregates Classification

317
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
317
Classification of Leukocytes01:30

Classification of Leukocytes

1.9K
Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
1.9K
Classification of Signals01:30

Classification of Signals

453
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
453

You might also read

Related Articles

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

Sort by
Same author

Baseline Characteristics of Patients with Asthma Initiating Dupilumab in a Real-World Setting: The REVEAL Registry.

Advances in therapy·2026
Same author

Right-Sided Empyema Thoracis Mimicking Pulmonary Tuberculosis Secondary to a Complicated Hepatic Cyst: A Diagnostic Pitfall.

Cureus·2026
Same author

Dupilumab attenuate IL-4 and IL-13 synergistic effect on airway remodeling in asthma via blocking IL-4Rα/STAT6 axis.

Scientific reports·2026
Same author

Availability and Costs of Allergic Rhinitis Treatments Across the World: A Survey of ARIA Experts.

Allergy·2026
Same author

Respiratory Syncytial Virus Immunization Strategies for Older Adults in Gulf Cooperation Council Countries: Expert Consensus and Recommendations.

Infectious diseases and therapy·2026
Same author

Development and Validation of the Risk of Exacerbation in Severe Asthma (RESA) Model.

The journal of allergy and clinical immunology. In practice·2026

Related Experiment Video

Updated: Jun 27, 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

737

COVID-19 Hierarchical Classification Using a Deep Learning Multi-Modal.

Albatoul S Althenayan1,2, Shada A AlSalamah1,3,4, Sherin Aly5

  • 1Information Systems Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.

Sensors (Basel, Switzerland)
|April 27, 2024
PubMed
Summary

This study developed an accurate deep learning model using chest X-ray images and medical data to diagnose COVID-19 pneumonia, distinguishing it from other lung conditions. The multi-modal approach enhances radiological diagnostics for faster patient isolation and treatment.

Keywords:
COVID-19CXRartificial intelligencedeep learningdiagnosishierarchicalimage classificationmulti-classesmulti-modalpneumonia

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
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

4.0K

Related Experiment Videos

Last Updated: Jun 27, 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

737
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
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

4.0K

Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Deep Learning for Disease Diagnosis

Background:

  • Coronavirus disease 2019 (COVID-19) presents diagnostic challenges due to time constraints, limited radiologist availability, and RT-PCR limitations.
  • Existing deep learning models for COVID-19 classification often use binary methods, single modalities, small datasets, and neglect the hierarchical nature of pneumonia classification.
  • Accurate and timely diagnosis of COVID-19 is crucial for patient isolation and effective public health management.

Purpose of the Study:

  • To develop a multi-modal deep learning approach for accurate COVID-19 pneumonia identification and differentiation from other pneumonia types and healthy lungs.
  • To demonstrate the value of integrating chest X-ray (CXR) images with tabular medical data for improved diagnostic accuracy.
  • To address limitations of previous studies by utilizing a hierarchical classification structure and generating synthetic data for imbalanced datasets.

Main Methods:

  • Employed Resnet-based and VGG-based pre-trained convolutional neural network (CNN) models for feature extraction from CXR images.
  • Utilized early fusion to combine features from CNN models and tabular medical data for classifying eight distinct classes.
  • Leveraged a hierarchical classification structure and generative adversarial networks (GANs) to handle imbalanced datasets and improve classification outcomes.

Main Results:

  • The proposed multi-modal deep learning approach achieved a macro-average F1-score of 95.9% on private datasets.
  • COVID-19 identification specifically reached an F1-score of 87.5% using a Resnet-based structure.
  • The integration of tabular medical data significantly contributed to the enhanced diagnostic accuracy of the model.

Conclusions:

  • A highly accurate deep learning multi-modal system was created for diagnosing COVID-19 and differentiating it from other pneumonia types and normal lung conditions.
  • The developed model effectively enhances the radiological diagnostic process, enabling more timely and accurate identification of COVID-19.
  • This research highlights the potential of combining imaging and clinical data with advanced AI techniques for robust disease diagnosis.