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

Thymosin Alpha-1 Restores Chemotherapy-Induced Antitumor Immunity by Chaperoning a MicroRNA Ligand of TLR7 in Dendritic Cells.

Cancer research·2026
Same author

PMF-YOLO: a parameter-efficient multi-scale detector for underwater optical imagery under scattering and nonuniform illumination.

Applied optics·2026
Same author

Dual-branch underwater image enhancement network based on fusion of polarization and color information.

Applied optics·2026
Same author

The Expression of ABC and MFS Transporter Genes, Not Mutations at <i>ScCyp51</i>, Is Associated with Difenoconazole Resistance in <i>Stagonosporopsis Cucurbitacearum</i> from Melon Fields.

Journal of agricultural and food chemistry·2026
Same author

Policy-Guided Model Predictive Path Integral for Safe Manipulator Trajectory Planning.

Sensors (Basel, Switzerland)·2026
Same author

Polarization-guided diffusion model for physically inspired underwater image descattering.

Applied optics·2026

Related Experiment Video

Updated: Dec 13, 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

2.3K

A Novel Pulmonary Nodule Detection Model Based on Multi-Step Cascaded Networks.

Jianning Chi1, Shuang Zhang1, Xiaosheng Yu1

  • 1Faculty of Robot Science and Engineering, Northeastern University, No. 195, Chuangxin Road, Shenyang 110169, China.

Sensors (Basel, Switzerland)
|August 6, 2020
PubMed
Summary

This study introduces a novel deep convolutional neural network for accurate pulmonary nodule detection in chest CT scans, improving early lung cancer diagnosis. The new framework enhances accuracy, sensitivity, and specificity compared to existing methods.

Keywords:
deep neural convolutional networkdense connectiondilated convolutioninception structuremulti-resolution convolutionpulmonary nodule detection

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

1.9K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.2K

Related Experiment Videos

Last Updated: Dec 13, 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

2.3K
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

1.9K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.2K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Pulmonary nodule detection in chest CT is crucial for early lung cancer diagnosis.
  • Current computer-assisted methods struggle with differentiating nodules from benign structures.
  • Accurate nodule detection remains a significant challenge in medical imaging analysis.

Purpose of the Study:

  • To propose a novel deep convolutional neural network (DCNN) framework for enhanced pulmonary nodule detection in chest CT images.
  • To improve the accuracy and reliability of automated nodule detection systems.
  • To address the limitations of existing methods in distinguishing true nodules from confounding factors.

Main Methods:

  • A three-cascaded U-net network framework was developed for a coarse-to-fine detection process.
  • The framework integrates inception structures, dense skip connections, dilated convolutions, and multi-scale pooling for robust feature extraction and detection.
  • Combined focal loss, perceptual loss, and dice loss were employed to handle imbalanced sample distributions.

Main Results:

  • The proposed DCNN framework demonstrated superior performance in pulmonary nodule detection on public datasets.
  • Experimental results showed significant improvements in accuracy, sensitivity, and specificity compared to state-of-the-art methods.
  • The method effectively overcomes challenges posed by calcifications, vessels, and other benign lung structures.

Conclusions:

  • The novel DCNN framework offers a promising solution for accurate and reliable pulmonary nodule detection in chest CT.
  • This advancement has the potential to significantly aid in the early diagnosis of lung cancer.
  • The proposed approach represents a substantial improvement over existing computer-assisted detection techniques.