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

Pulmonary Hypertension: Classification and Pathogenesis01:30

Pulmonary Hypertension: Classification and Pathogenesis

180
Pulmonary hypertension (PH) is a severe health condition in which the mean pulmonary arterial pressure increases to 25 mmHg or more, even when the body is at rest. This high pressure in the blood vessels that transport blood from the heart to the lungs can cause various symptoms, including shortness of breath, can lead to right heart failure, and significantly affect the overall quality of life.
There are various classifications for PH, each relating to different underlying causes and also...
180
Chronic Obstructive Pulmonary Disease01:22

Chronic Obstructive Pulmonary Disease

1.2K
COPD is defined as a heterogeneous lung condition marked by persistent respiratory symptoms such as dyspnea, cough, and sputum production, caused by abnormalities in the airways that cause airflow obstruction.
Smoking is a primary risk factor for COPD, with over 80% of patients having a history of it. Patients typically experience progressive dyspnea or labored breathing, frequent coughing, and recurrent pulmonary infections. Many eventually succumb to respiratory failure, characterized by...
1.2K
Pulmonary Tuberculosis II01:28

Pulmonary Tuberculosis II

236
Tuberculosis, or TB, is a bacterial infectious disease caused by Mycobacterium tuberculosis. While its primary impact is on the lungs, leading to pulmonary tuberculosis, it can also affect various other organs, a condition referred to as extrapulmonary tuberculosis.
Here is a detailed explanation of its pathophysiology:
Transmission: The process begins when a person inhales droplet nuclei containing M. tuberculosis. These are typically released into the air when an individual with pulmonary or...
236

You might also read

Related Articles

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

Sort by
Same author

Knowledge and Perception of Cervical Cancer and Pap-Smear Screening Among Antenatal Women in Ogun State, Nigeria.

Cancer medicine·2026
Same author

LExNet: A bio-inspired lightweight ensemble model for breast cancer classification using hybrid autoencoder and swarm intelligence optimization.

Digital health·2026
Same author

Interpretable CRAM‑Enhanced Lightweight Dual‑Branch CNN for Real‑Time Breast Cancer Histopathology in Internet‑of‑Medical‑Things Environments.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

NeuroMorphFusion: A Neuro-Inspired Hybrid Learning Framework for Interpretable Deep Lesion Detection in IoT-Enabled Healthcare Systems.

Technology in cancer research & treatment·2026
Same author

Autoencoder-Assisted Stacked Ensemble Learning for Lymphoma Subtype Classification: A Hybrid Deep Learning and Machine Learning Approach.

Tomography (Ann Arbor, Mich.)·2025
Same author

Mobile app review analysis for crowdsourcing of software requirements: a mapping study of automated and semi-automated tools.

PeerJ. Computer science·2024

Related Experiment Video

Updated: Jul 2, 2025

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

1.8K

PulmoNet: a novel deep learning based pulmonary diseases detection model.

AbdulRahman Tosho Abdulahi1, Roseline Oluwaseun Ogundokun2,3, Ajiboye Raimot Adenike4

  • 1Department of Computer Science, Institute of Information and Communication Technology, Kwara State Polytechnic, Ilorin, Nigeria.

BMC Medical Imaging
|February 28, 2024
PubMed
Summary

This study introduces a deep convolutional neural network model for detecting pulmonary diseases like COVID-19 and pneumonia using radiography. The AI model achieves high accuracy, offering a cost-effective diagnostic solution, especially for developing nations.

Keywords:
AccuracyCT scanDeep convolutional neural networkMachine learningPulmonary diseasesX-ray

More Related Videos

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

1.4K
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: Jul 2, 2025

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

1.8K
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

1.4K
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:

  • Pulmonary diseases pose significant health challenges, varying from common colds to life-threatening conditions like pneumonia and COVID-19.
  • Accurate and timely diagnosis of pulmonary infections is critical, yet costly, particularly in developing countries.
  • Radiography, including X-ray and CT scans, aids in detecting these infections, driving the need for advanced analytical methods.

Purpose of the Study:

  • To develop and evaluate a deep convolutional neural network (DCNN) model for detecting three distinct pulmonary diseases: COVID-19, bacterial pneumonia (BP), and viral pneumonia (VP).
  • To optimize the DCNN model using image augmentation techniques for enhanced detection accuracy.
  • To assess the model's performance against traditional methods for pulmonary disease recognition from radiographic images.

Main Methods:

  • A deep convolutional neural network (DCNN) model was designed and implemented for image-based detection of pulmonary diseases.
  • Image augmentation techniques were employed to optimize the DCNN model's training and performance.
  • The model was trained and tested on a dataset comprising 10,325 healthy cases, 3,749 COVID-19 cases, 883 bacterial pneumonia cases, and 1,478 viral pneumonia cases.

Main Results:

  • The DCNN model demonstrated high detection accuracy, achieving averages of 94% for COVID-19, 95.4% for bacterial pneumonia, 99.4% for viral pneumonia, and 98.30% overall.
  • The model exhibited efficient training and detection times, approximately 60 seconds for training and 50 seconds for detection.
  • The proposed DCNN approach showed superior performance compared to traditional texture descriptor techniques for pulmonary disease recognition.

Conclusions:

  • The developed DCNN model shows significant potential for accurate and efficient detection of key pulmonary diseases from radiographic images.
  • This AI-driven approach offers a promising advancement in medical diagnostics, potentially improving healthcare accessibility and outcomes, especially in resource-limited settings.
  • The study highlights the effectiveness of deep learning in enhancing the early identification of critical respiratory conditions like COVID-19 and pneumonia.