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

Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and the...
Imaging Studies for Cardiovascular System III: X-Ray01:20

Imaging Studies for Cardiovascular System III: X-Ray

The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
Definition and Purpose
An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...

You might also read

Related Articles

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

Sort by
Same author

A Novel Ensemble Meta-Model for Enhanced Retinal Blood Vessel Segmentation Using Deep Learning Architectures.

Biomedicines·2025
Same author

Hospital Re-Admission Prediction Using Named Entity Recognition and Explainable Machine Learning.

Diagnostics (Basel, Switzerland)·2024
Same author

Explainable COVID-19 Detection Based on Chest X-rays Using an End-to-End RegNet Architecture.

Viruses·2023
Same author

Deep Learning Methods for Chest Disease Detection Using Radiography Images.

SN computer science·2023
Same author

DCTable: A Dilated CNN with Optimizing Anchors for Accurate Table Detection.

Journal of imaging·2023
Same author

Applying Deep Learning for Breast Cancer Detection in Radiology.

Current oncology (Toronto, Ont.)·2022
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 30, 2026

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

14.3K

A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography.

Adnane Ait Nasser1, Moulay A Akhloufi1

  • 1Perception, Robotics and Intelligent Machines Research Group (PRIME), Department of Computer Science, Université de Moncton, Moncton, NB E1C 3E9, Canada.

Diagnostics (Basel, Switzerland)
|January 8, 2023
PubMed
Summary
This summary is machine-generated.

Deep learning (DL) models enhance chest X-ray (CXR) analysis for faster, more accurate lung disease detection. This review details datasets, preprocessing, DL models, and future research directions for improved computer-aided diagnosis.

Keywords:
chest X-raycomputer-aided detectiondeep convolutional neural networksdeep learningmachine learningradiography

More Related Videos

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
10:26

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

2.0K
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

Related Experiment Videos

Last Updated: Jun 30, 2026

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

14.3K
Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
10:26

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

2.0K
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

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Radiology

Background:

  • Chest X-ray radiography (CXR) is vital for detecting life-threatening diseases, but visual interpretation faces challenges due to similar disease patterns, leading to potential misdiagnosis.
  • Computer-aided detection (CAD) using machine learning (ML) and deep learning (DL) offers promising solutions for efficient and rapid diagnosis of thoracic diseases from CXR images.

Purpose of the Study:

  • To provide a comprehensive review of deep learning models and techniques for detecting chest diseases in CXR images.
  • To detail publicly available CXR datasets, preprocessing methods, and various DL architectures used in computer-aided detection.
  • To discuss current challenges, the importance of interpretability, and future research directions in DL-based CXR analysis.

Main Methods:

  • Review of recent literature on deep learning models (VGG, ResNet, DenseNet, Inception, EfficientNet, RetinaNet, ensemble methods) applied to CXR analysis.
  • Summary of CXR image preprocessing techniques including enhancement, segmentation, bone suppression, and data augmentation.
  • Analysis of challenges, interpretability, and explainability in DL models for chest disease detection.

Main Results:

  • Deep learning models demonstrate significant potential in improving the efficiency and accuracy of diagnosing chest diseases from CXR.
  • Various DL architectures and preprocessing techniques are effective in enhancing image quality and addressing data imbalance.
  • The review highlights the need for interpretable and explainable AI models in medical imaging.

Conclusions:

  • Deep learning techniques are revolutionizing computer-aided detection of chest diseases in CXR, offering faster and potentially more accurate diagnoses.
  • Further research focusing on interpretability, explainability, and robust model development is crucial for clinical translation.
  • This review serves as a guide for researchers to develop advanced models for early and automatic detection of chest diseases.