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 Tuberculosis IV01:26

Pulmonary Tuberculosis IV

216
Tuberculosis, more commonly referred to as TB, is an infectious disease stemming from Mycobacterium tuberculosis. While it primarily impacts the lungs, TB can also affect other body areas. Given its severity and global impact, timely and accurate diagnosis is crucial for controlling its spread and improving patient outcomes.
Several diagnostic approaches are used to detect TB. The conventional method is the Tuberculin Skin Test (TST), also known as the Mantoux test. However, this method has...
216
Pulmonary Tuberculosis I01:29

Pulmonary Tuberculosis I

339
Tuberculosis, often called TB, is a contagious illness primarily caused by Mycobacterium tuberculosis. It mainly affects the lung parenchyma but can also impact other body parts.
Causative Organism
The primary infectious agent causing tuberculosis is Mycobacterium tuberculosis, a slow-growing, acid-fast, aerobic rod that exhibits sensitivity to heat and ultraviolet light. Instances of Mycobacterium bovis and Mycobacterium avium contributing to the development of TB infection are rare.
Mode of...
339
Pulmonary Tuberculosis II01:28

Pulmonary Tuberculosis II

426
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...
426
Pulmonary Tuberculosis III01:31

Pulmonary Tuberculosis III

469
Tuberculosis (TB) is a contagious infection primarily affecting the lung parenchyma but which can also affect other body parts. TB can be classified based on disease development, presentation, and the affected anatomical site.
The first classification is based on the development of the disease, and it includes the following categories:
469
Pulmonary Tuberculosis V01:28

Pulmonary Tuberculosis V

253
Medical management of tuberculosis (TB) patients involves a comprehensive approach that includes diagnosis, treatment, and monitoring. The specific strategies can vary depending on the type of tuberculosis (latent or active), the patient's overall health status, and other considerations.
Latent tuberculosis infection occurs when TB bacteria are present in a person's body, but are not causing illness or symptoms. It is not contagious, and preventive treatment is crucial to avoid the...
253

You might also read

Related Articles

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

Sort by
Same author

An Explainable Multi-Scale Deep Learning Framework for Multi-Class Brain MRI Classification.

Diagnostics (Basel, Switzerland)·2026
Same author

Lightweight deep learning for medical imaging using MobileNetV2-based brain pathology classification with Grad-CAM interpretability.

Frontiers in medicine·2026
Same author

A Dual-Branch Frequency-Aware Attention Framework for Rare Neurological Disease Classification from Brain MRI.

Diagnostics (Basel, Switzerland)·2026
Same author

Correction: An efficient Alzheimer's disease detection by NV classifier with BWTDL approach using MRI image.

BMC medical imaging·2026
Same author

Privacy-Preserving Hybrid GA-LSTM Ensemble for Typhoid Detection Using Optimised Clinical Feature Selection.

Biomedicines·2026
Same author

CT-Malaria Detection via Adaptive-Weighted Deep Learning Models.

Biomedicines·2026
Same journal

RETRACTION: An IoMT-Based Approach for Real-Time Monitoring Using Wearable Neuro-Sensors.

Journal of healthcare engineering·2026
Same journal

RETRACTION: Learning to Discriminate Adversarial Examples by Sensitivity Inconsistency in IoHT Systems.

Journal of healthcare engineering·2026
Same journal

RETRACTION: Multi-Chaos-Based Lightweight Image Encryption-Compression for Secure Occupancy Monitoring.

Journal of healthcare engineering·2026
Same journal

RETRACTION: Image Risk Assessment of the Thyroid Cancer Model Based on Discriminant Analysis and the Value of TAP and CEA Combined Detection.

Journal of healthcare engineering·2026
Same journal

RETRACTION: Meta-Analysis of the Prognostic Value of Narcotrend Monitoring of Different Depths of Anesthesia and Different Bispectral Index (BIS) Values for Cognitive Dysfunction after Tumor Surgery in Elderly Patients.

Journal of healthcare engineering·2026
Same journal

Correction to "Representation of Differential Learning Method for Mitosis Detection".

Journal of healthcare engineering·2026
See all related articles

Related Experiment Video

Updated: Sep 25, 2025

A 3D Human Lung Tissue Model for Functional Studies on Mycobacterium tuberculosis Infection
10:10

A 3D Human Lung Tissue Model for Functional Studies on Mycobacterium tuberculosis Infection

Published on: October 5, 2015

19.1K

Tuberculosis Disease Diagnosis Based on an Optimized Machine Learning Model.

Olfa Hrizi1, Karim Gasmi1, Ibtihel Ben Ltaifa2

  • 1Department of Computer Science, College of Arts and Sciences at Tabarjal, Jouf University, Jouf, Saudi Arabia.

Journal of Healthcare Engineering
|May 2, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an optimized machine learning model for tuberculosis diagnosis using genetic algorithms and support vector machines. The approach enhances diagnostic accuracy by selecting optimal image features, improving healthcare quality.

More Related Videos

Use of the Invertebrate Galleria mellonella as an Infection Model to Study the Mycobacterium tuberculosis Complex
09:23

Use of the Invertebrate Galleria mellonella as an Infection Model to Study the Mycobacterium tuberculosis Complex

Published on: June 30, 2019

12.1K
The MODS method for diagnosis of tuberculosis and multidrug resistant tuberculosis
23:06

The MODS method for diagnosis of tuberculosis and multidrug resistant tuberculosis

Published on: August 11, 2008

19.2K

Related Experiment Videos

Last Updated: Sep 25, 2025

A 3D Human Lung Tissue Model for Functional Studies on Mycobacterium tuberculosis Infection
10:10

A 3D Human Lung Tissue Model for Functional Studies on Mycobacterium tuberculosis Infection

Published on: October 5, 2015

19.1K
Use of the Invertebrate Galleria mellonella as an Infection Model to Study the Mycobacterium tuberculosis Complex
09:23

Use of the Invertebrate Galleria mellonella as an Infection Model to Study the Mycobacterium tuberculosis Complex

Published on: June 30, 2019

12.1K
The MODS method for diagnosis of tuberculosis and multidrug resistant tuberculosis
23:06

The MODS method for diagnosis of tuberculosis and multidrug resistant tuberculosis

Published on: August 11, 2008

19.2K

Area of Science:

  • Computer science applications in healthcare
  • Machine learning for medical diagnostics
  • Medical imaging analysis

Background:

  • Computer science is vital for modern healthcare systems, aiding collaboration in diagnostics.
  • Innovations improving diagnostic accuracy and safety are crucial for healthcare advancement.
  • Early disease diagnosis, including tuberculosis (TB), is a key objective.

Purpose of the Study:

  • To develop an optimized machine learning model for tuberculosis diagnosis.
  • To extract optimal texture features from TB-related images and select classifier hyperparameters.
  • To improve diagnostic accuracy while minimizing extracted features, addressing a multitask optimization challenge.

Main Methods:

  • Utilized a genetic algorithm (GA) for optimal feature selection.
  • Employed a support vector machine (SVM) classifier with selected features.
  • Conducted experiments using the ImageCLEF 2020 dataset.

Main Results:

  • Achieved significantly higher accuracy compared to state-of-the-art methods.
  • Demonstrated superior outcomes in TB diagnosis using the proposed approach.
  • Highlighted the efficiency of the modified SVM classifier over standard ones.

Conclusions:

  • The proposed optimized machine learning model effectively improves tuberculosis diagnosis.
  • The combination of GA for feature selection and SVM for classification is highly efficient.
  • This approach contributes to advancing diagnostic capabilities in medical imaging.