Jove
Visualize
Contact Us

Related Concept Videos

Classification of Connective Tissues01:30

Classification of Connective Tissues

10.6K
The connective tissues have different properties and functions in the human body. They are broadly categorized into proper, supporting, or fluid connective tissues.
Connective Tissue Proper
Connective tissue proper is the most abundant class of connective tissues. As its name implies, it predominantly connects different tissues in the body. Depending on the cell types, ground substance, viscosity, and fiber types in the ECM, connective tissue proper is further categorized into loose and dense....
10.6K

You might also read

Related Articles

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

Sort by
Same author

An Efficient Dual-Sampling Approach for Chest CT Diagnosis.

Journal of multidisciplinary healthcare·2025
Same author

Lossless compression-based detection of osteoporosis using bone X-ray imaging.

Journal of X-ray science and technology·2024
Same author

Generative and Discriminative Learning for Lung X-Ray Analysis Based on Probabilistic Component Analysis.

Journal of multidisciplinary healthcare·2023
Same author

The cranial capacity of the Saudi population measured using 3D computed tomography scans.

Neurosciences (Riyadh, Saudi Arabia)·2023
Same author

A computational approach for analysis of intratumoral heterogeneity and standardized uptake value in PET/CT images1.

Journal of X-ray science and technology·2023
Same author

Chest CT utilization in COVID-19: a dosimetric and diagnostic-quality study.

Radiation protection dosimetry·2023
Same journal

A Critical Review of PET/CT Assessment After CAR-T Cell Therapy in Large B-Cell Lymphoma, Limitations of Current Criteria, Emerging Biomarkers, and a Framework for Standardised Evaluation.

Journal of multidisciplinary healthcare·2026
Same journal

Machine Learning-Based Prediction of Prolonged Mechanical Ventilation After Stanford Type A Aortic Dissection Surgery.

Journal of multidisciplinary healthcare·2026
Same journal

Gamification-Based Interventions for Treatment Adherence and Self-Management in Pediatric Chronic Diseases: A Scoping Review with Implications for Thalassemia.

Journal of multidisciplinary healthcare·2026
Same journal

Effectiveness of BHATIN (Behavior-Tailored Intervention) for Self-Care Management and Clinical Biomarkers Among Patients with Hypertension: A Quasi Experimental Study [Response to Letter].

Journal of multidisciplinary healthcare·2026
Same journal

Factors Influencing Functional Recovery in People After Chronic Critical Illness During Early Neurological Rehabilitation.

Journal of multidisciplinary healthcare·2026
Same journal

Nutritional Support Strategies for Refeeding Syndrome in ICU Patients: A Review of Current Evidence.

Journal of multidisciplinary healthcare·2026
See all related articles
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 Experiment Video

Updated: Jun 28, 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

Classification of Chest CT Lung Nodules Using Collaborative Deep Learning Model.

Khalaf Alshamrani1,2, Hassan A Alshamrani1

  • 1Radiological Sciences Department, Najran University, Najran, Saudi Arabia.

Journal of Multidisciplinary Healthcare
|April 10, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a collaborative deep learning (CDL) model for accurate lung nodule classification in chest CT scans. The CDL model achieves 93.24% accuracy, improving early lung cancer detection with limited data.

Keywords:
CT imagescollaborative deep learninglogistic regressionlung cancernodulesradial lengthstandard deviation

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: Jun 28, 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 analysis
  • Artificial intelligence in healthcare
  • Oncology research

Background:

  • Early lung cancer detection via chest CT scans is crucial for patient survival.
  • Deep learning advancements face challenges in nodule detection due to limited training data.

Purpose of the Study:

  • To develop a collaborative deep learning (CDL) model for differentiating malignant from non-cancerous lung nodules.
  • To address the challenge of insufficient training datasets in lung nodule classification.

Main Methods:

  • The CDL model dissects nodules into six characteristic parts for detailed feature learning.
  • A CDL submodel uses six feature patch types to fine-tune a ResNet-50 network.
  • An adaptive weighting method, refined via error backpropagation, enhances nodule identification accuracy.

Main Results:

  • The CDL model achieved a high classification accuracy of 93.24% for lung nodules.
  • This performance significantly surpasses current state-of-the-art methods.

Conclusions:

  • The CDL model offers a promising solution for accurate malignant lung nodule detection with limited data.
  • This approach enhances diagnostic accuracy, aiding early lung cancer detection and treatment.