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 Experiment Video

Updated: Jun 29, 2026

Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules
07:53

Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules

Published on: October 13, 2023

Synergistic Deep Learning Fusion for Precision Lung Cancer Staging.

Sinthia P1, Anitha Juliette Albert2, Malathi M3

  • 1Department of Biomedical Engineering, Saveetha Engineering College, Chennai, Tamil Nadu, India.

Asian Pacific Journal of Cancer Prevention : APJCP
|June 25, 2026
PubMed
Summary

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

Integrating Ant Colony Optimization with Deep Learning for Improved Lung Cancer Diagnosis and Prognosis.

Asian Pacific journal of cancer prevention : APJCP·2026
Same author

Oral Cancer Prediction Using a Probability Neural Network (PNN).

Asian Pacific journal of cancer prevention : APJCP·2023
Same author

Segmentation of CT Lung Images Using FCM with Active Contour and CNN Classifier.

Asian Pacific journal of cancer prevention : APJCP·2022
Same author

Brain Tumour Segmentation Using Convolutional Neural Network with Tensor Flow.

Asian Pacific journal of cancer prevention : APJCP·2019
Same author

NULL convention floating point multiplier.

TheScientificWorldJournal·2015
Same journal

Genomic Landscape of Oral Squamous Cell Carcinoma in Never Smokers and Never Drinkers.

Asian Pacific journal of cancer prevention : APJCP·2026
Same journal

Gut Microbiota Modulation via Synbiotics: A Perspective for Boosting Antitumor Immunity and Inactivating Carcinogens in Early Life.

Asian Pacific journal of cancer prevention : APJCP·2026
Same journal

Temporal Hematologic Alterations in Women Receiving Pharmacotherapy for Breast Cancer: A Prospective Analysis.

Asian Pacific journal of cancer prevention : APJCP·2026
Same journal

Upstaging of Operable Adenocarcinoma of the Stomach and Gastroesophageal Junction Following Staging Laparoscopy (SL): High-Risk Clinicopathological Features Requisite for Mandatory SL.

Asian Pacific journal of cancer prevention : APJCP·2026
Same journal

Gene Expression Alterations of TIMP3, ELASTIN, K-RAS, and BRAF in Colorectal Cancer Patients with H. pylori Infection.

Asian Pacific journal of cancer prevention : APJCP·2026
Same journal

Vitamin D3 Supplementation Modulates C-MYC/VEGF and Improves Chemotherapy Outcomes in Metastatic CRC Patients: Integrated Clinical-Mechanistic Evidence.

Asian Pacific journal of cancer prevention : APJCP·2026
See all related articles
This summary is machine-generated.

A deep learning model accurately stages lung cancer using CT scans, improving early detection. This automated system aids clinical decisions by precisely classifying cancer stages from medical images.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Lung cancer staging is crucial for treatment planning.
  • Accurate staging relies on interpreting complex medical images like CT scans.
  • Manual staging can be time-consuming and prone to variability.

Purpose of the Study:

  • To develop an automated deep learning system for lung cancer staging.
  • To evaluate the system's performance using computed tomography (CT) scan images.
  • To enhance early lung cancer detection and clinical decision-making.

Main Methods:

  • Utilized a large dataset (LIDC-IDRI/TCIA) of 1,018 CT scans.
  • Preprocessed images using segmentation, filtering, and augmentation.
  • Trained a custom Convolutional Neural Network (CNN) for multi-stage classification.
Keywords:
Cancer stage classificationClinical decision supportComputer-aided diagnosisFeature Extractionearly detection

Related Experiment Videos

Last Updated: Jun 29, 2026

Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules
07:53

Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules

Published on: October 13, 2023

Main Results:

  • Achieved high Area Under the Curve (AUC) scores for all stages (0.95-0.98).
  • Overall classification accuracy reached 93.0% (95% CI: 91.2-94.8).
  • Demonstrated superior performance over baseline CNNs and comparable/better results than state-of-the-art methods.

Conclusions:

  • The proposed CNN architecture effectively and precisely classifies lung cancer stages from CT images.
  • The automated system supports clinical decision-making.
  • This technology enhances the early detection of lung cancer.