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 journal

Quantifying the development of visual expertise in medical image interpretation using fractal eye-gaze metrics.

Medical & biological engineering & computing·2026
Same journal

Robot-assisted laser osteotomy system based on dose-effect model-guided feedforward control.

Medical & biological engineering & computing·2026
Same journal

A randomized multi-window 3D deep learning approach for intracranial hemorrhage detection on non-contrast head CT.

Medical & biological engineering & computing·2026
Same journal

A novel SE-ResNet architecture for continuous estimation of wrist and hand movements from HD-sEMG.

Medical & biological engineering & computing·2026
Same journal

Anti-aliasing-enhanced WaveUNet for clinically reliable 12-lead ECG reconstruction from limited 3-lead input.

Medical & biological engineering & computing·2026
Same journal

Deep multi-modal features based spatio-temporal video regression for non-invasive hemoglobin estimation.

Medical & biological engineering & computing·2026

Related Experiment Video

Updated: Sep 24, 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

2.1K

An improved CNN-based architecture for automatic lung nodule classification.

Sozan Abdullah Mahmood1, Hunar Abubakir Ahmed2

  • 1Computer Department, College of Science, University of Sulaimani, Sulaymaniyah, 46001, Kurdistan, Iraq. sozan.mahmood@univsul.edu.iq.

Medical & Biological Engineering & Computing
|May 6, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel CNN-based system for accurate lung cancer diagnosis, classifying pulmonary nodules as benign or malignant. The advanced model significantly improves early detection rates, enhancing patient survival chances.

Keywords:
Computer-aided diagnosisConvolutional neural networkDeep learningLung nodule classification

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

1.6K
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

2.0K

Related Experiment Videos

Last Updated: Sep 24, 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

2.1K
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

1.6K
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

2.0K

Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Oncology
  • Computer-Aided Diagnosis

Background:

  • Lung cancer poses a significant global health threat, with high mortality rates.
  • Early diagnosis is crucial for improving patient survival but relies on time-consuming and error-prone expert interpretation of CT scans.
  • Distinguishing benign from malignant pulmonary nodules is challenging, often leading to invasive biopsies for non-cancerous cases.

Purpose of the Study:

  • To develop and evaluate a Convolutional Neural Network (CNN)-based computer-aided diagnosis (CAD) system.
  • To automatically classify pulmonary nodules as either benign or malignant using medical imaging data.
  • To enhance the accuracy and efficiency of lung cancer diagnosis, reducing the need for invasive procedures.

Main Methods:

  • The study proposes a novel CNN architecture, adapted from the AlexNet model, incorporating modifications in layer ordering, hyperparameters, and functions.
  • Extensive pre-processing steps, including segmentation, normalization, and zero centering, were applied to the dataset to optimize model training.
  • The system was trained and validated on a dataset of pulmonary nodule images.

Main Results:

  • The proposed CNN-based system achieved high performance metrics: 98.7% accuracy, 98.6% sensitivity, and 98.9% specificity.
  • The modified AlexNet architecture demonstrated superior performance compared to the original AlexNet model.
  • The system effectively classified pulmonary nodules, showing high sensitivity in nodule analysis.

Conclusions:

  • The developed CNN-based CAD system offers a highly accurate and efficient method for classifying pulmonary nodules.
  • This automated approach has the potential to significantly improve early lung cancer detection and reduce unnecessary invasive procedures.
  • The modified AlexNet architecture provides a robust framework for advanced medical image analysis in oncology.