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

You might also read

Related Articles

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

Sort by
Same author

Efficient preconditioning strategies for accelerating GMRES in block-structured nonlinear systems for image deblurring.

PloS one·2025
Same author

Anomaly Detection in Embryo Development and Morphology Using Medical Computer Vision-Aided Swin Transformer with Boosted Dipper-Throated Optimization Algorithm.

Bioengineering (Basel, Switzerland)·2024
Same author

Obstacle Detection System for Navigation Assistance of Visually Impaired People Based on Deep Learning Techniques.

Sensors (Basel, Switzerland)·2023
Same author

Indoor Signs Detection for Visually Impaired People: Navigation Assistance Based on a Lightweight Anchor-Free Object Detector.

International journal of environmental research and public health·2023
Same author

Analysis of MIR27A (rs11671784) Variant Association with Systemic Lupus Erythematous.

Life (Basel, Switzerland)·2023
Same author

HSP70 Expression Signature in Renal Cell Carcinoma: A Clinical and Bioinformatic Analysis Approach.

Genes·2023
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: Aug 5, 2025

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

A CAD System for Lung Cancer Detection Using Hybrid Deep Learning Techniques.

Ahmed A Alsheikhy1, Yahia Said1, Tawfeeq Shawly2

  • 1Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 91431, Saudi Arabia.

Diagnostics (Basel, Switzerland)
|March 29, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an automated system using deep learning for early lung cancer detection and classification. The integrated VGG-19 and LSTMs model achieved over 99% accuracy, aiding physicians in diagnosis.

Keywords:
CADDCNNLSTMsVGG-19artificial intelligenceclassificationdiagnosislung cancermedical informatics

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

1.9K
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: Aug 5, 2025

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
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

1.9K
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
  • Oncology

Background:

  • Lung cancer is the leading cause of cancer deaths globally, necessitating early detection.
  • Current diagnostic methods can be improved for accuracy and efficiency.

Purpose of the Study:

  • To develop and evaluate a fully automated computer-aided diagnosis (CAD) system for lung cancer identification and classification.
  • To enhance early detection rates and reduce mortality.

Main Methods:

  • Utilized a deep convolutional neural network (DCNN) VGG-19 model integrated with long short-term memory networks (LSTMs).
  • Applied image segmentation techniques for enhanced feature extraction.
  • Trained and validated the system on three diverse lung cancer datasets (Kaggle, LUNA16).

Main Results:

  • The integrated VGG-19 and LSTMs model achieved an average accuracy of 99.42%.
  • Achieved high performance metrics: 99.76% recall, 99.88% precision, and 99.82% F-score.
  • Outperformed existing methods in comparative evaluations.

Conclusions:

  • The proposed automated system demonstrates high accuracy and effectiveness in diagnosing lung cancer.
  • This model shows significant potential for clinical deployment to assist physicians.
  • The system offers a valuable and competent tool for accurate lung cancer diagnosis.