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

Deconvolution01:20

Deconvolution

349
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
349
Aggregates Classification01:29

Aggregates Classification

432
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...
432
Classification of Leukocytes01:30

Classification of Leukocytes

4.0K
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...
4.0K
Classification of Signals01:30

Classification of Signals

1.0K
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...
1.0K
Classification of Illness01:17

Classification of Illness

8.1K
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...
8.1K
Classification of Systems-I01:26

Classification of Systems-I

377
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:
377

You might also read

Related Articles

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

Sort by
Same author

CDH4/UBA1/RBMX axis promotes polycystic ovary syndrome progression through YAP1 activation.

Cellular & molecular biology letters·2026
Same author

Automatic prompt-guided incremental fine-tuning for offset detection in radiotherapy patient positioning.

Physics in medicine and biology·2026
Same author

MTW-ICHNet: Multi-task Weakly Supervised Learning with Enhanced Feature Descriptor Learning for Intracranial Hemorrhage Diagnosis.

Journal of imaging informatics in medicine·2026
Same author

Collaborative multitasking framework for enhanced hippocampus segmentation and Alzheimer's disease classification.

Brain research·2025
Same author

3D brain tumor segmentation based on a novel nettree merging.

Computers in biology and medicine·2025
Same author

The evolution and functional characterization of transcription factors E2Fs in lamprey, Lethenteron reissneri.

Developmental and comparative immunology·2025
Same journal

Rapid personalisation of cardiovascular models using invasively measured right ventricular pressure.

Computers in biology and medicine·2026
Same journal

Biologically inspired mechanisms for enhancing robustness in EEG signal modeling: Challenges, opportunities, and perspectives.

Computers in biology and medicine·2026
Same journal

Machine learning-based detection of missed inspiratory efforts using esophageal pressure during noisy pressure support ventilation.

Computers in biology and medicine·2026
Same journal

A computational model of chemically- and mechanically-induced thrombus formation in cerebral aneurysms.

Computers in biology and medicine·2026
Same journal

An improved catch fish optimization based deep learning model for Parkinson disease classification using EEG signal.

Computers in biology and medicine·2026
Same journal

Assessing the robustness of evaluation metrics for synthetic ECG signal quality.

Computers in biology and medicine·2026
See all related articles

Related Experiment Video

Updated: Oct 31, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

729

COVID-19 deep classification network based on convolution and deconvolution local enhancement.

Lingling Fang1, Xin Wang1

  • 1Department of Computing and Information Technology, Liaoning Normal University, Dalian City, Liaoning Province, China.

Computers in Biology and Medicine
|June 28, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning model using CT scans to accurately detect COVID-19 pneumonia. The novel convolution-deconvolution network enhances lesion detection, improving diagnostic accuracy for the novel coronavirus disease.

Keywords:
COVID-19 classification networkConvolutionDeconvolutionEnhanced CT featuresROI extraction

More Related Videos

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

1.1K
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

1.9K

Related Experiment Videos

Last Updated: Oct 31, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

729
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

1.1K
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

1.9K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Traditional Corona Virus Disease 2019 (COVID-19) detection methods face challenges like delayed results and misdiagnosis, potentially increasing infection rates.
  • Distinguishing between asymptomatic positive and negative COVID-19 patients presents diagnostic difficulties, impacting clinical management.
  • Accurate and timely diagnosis is crucial for controlling the spread of COVID-19.

Purpose of the Study:

  • To propose a deep classification network model for enhanced detection of COVID-19 pneumonia using computed tomography (CT) scans.
  • To improve the accuracy of differentiating between positive and negative COVID-19 cases, especially in asymptomatic individuals.
  • To leverage convolution and deconvolution operations for local enhancement of lesion features in CT images.

Main Methods:

  • A deep classification network model employing convolution and deconvolution local enhancement was developed.
  • The model enhances contrast between COVID-19 lesions and surrounding tissue, extracting discriminative middle-level features.
  • The COVID-CT dataset, comprising 1460 images from 143 patients, was utilized for training (70%) and testing (30%).

Main Results:

  • The proposed model achieved high classification performance, demonstrating effective identification of COVID-19 pneumonia.
  • Key performance metrics included sensitivity (0.98), specificity (0.96), positive predictive value (PPV) (0.98), and negative predictive value (NPV) (0.97).
  • The algorithm showed superior classification precision across different convolution layers and learning rates when compared to state-of-the-art models.

Conclusions:

  • The developed deep classification network model significantly improves the accuracy of COVID-19 pneumonia detection from CT images.
  • The convolution-deconvolution approach effectively enhances lesion visibility and feature extraction, aiding in precise diagnosis.
  • This AI-driven method offers a promising tool for rapid and reliable COVID-19 diagnosis, potentially reducing diagnostic errors and disease spread.