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Related Experiment Video

Updated: Jun 27, 2026

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

An Intelligence-Based Hybrid CNN-GAT Framework Optimized by the Whale Optimization Algorithm for Clinical Lung Cancer

Abbas Mirzaei1, Aminreza Mohajerzadeh2, Babak Nouri-Moghaddam3

  • 1Department of Computer Engineering, Ard.C., Islamic Azad University, Ardabil, Iran. a.mirzaei.iau@gmail.com.

Journal of Imaging Informatics in Medicine
|June 22, 2026
PubMed
Summary

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This summary is machine-generated.

A new hybrid deep learning model combining Convolutional Neural Networks (CNN) and Graph Attention Networks (GAT), optimized with the Whale Optimization Algorithm (WOA), significantly improves lung cancer detection from CT scans, achieving 98.7% accuracy.

Area of Science:

  • Artificial Intelligence in Medicine
  • Medical Imaging Informatics
  • Computational Pathology

Background:

  • Early and accurate lung cancer detection is crucial for effective treatment.
  • Interpreting complex chest CT data requires advanced imaging informatics solutions.
  • Current methods face challenges in accurately classifying lung cancer subtypes.

Purpose of the Study:

  • To develop a novel hybrid CNN-GAT framework for three-class lung cancer classification (benign, malignant, normal) from chest CT images.
  • To optimize the framework using the Whale Optimization Algorithm (WOA) for enhanced generalization and performance.
  • To evaluate the framework's accuracy, efficiency, and potential for clinical deployment.

Main Methods:

  • A hybrid CNN-GAT architecture utilizing a ResNet-18 backbone for feature extraction.
Keywords:
Chest CT imagingClinical decision support systemHybrid CNN–graph attention networkIntelligence-based medicineLung cancer diagnosisWhale Optimization Algorithm

Related Experiment Videos

Last Updated: Jun 27, 2026

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

  • Graph attention layers to model non-linear dependencies among localized CT features.
  • Whale Optimization Algorithm (WOA) for automated hyperparameter tuning (learning rate, batch size, etc.).

    • Main Results:

    • The proposed CNN-GAT + WOA framework achieved a test accuracy of 98.7%, precision of 98.5%, recall of 98.9%, and F1-score of 98.7%.
    • Achieved a Matthews correlation coefficient of 0.975 and an Area Under the Curve (AUC) of 0.994.
    • Demonstrated computational efficiency with ~4.1 million parameters and 0.035s inference time per scan.

    Conclusions:

    • The hybrid CNN-GAT + WOA model offers a highly accurate, generalizable, and efficient solution for lung cancer classification from CT scans.
    • The integration of graph-based intelligence and meta-heuristic optimization enhances diagnostic capabilities.
    • The framework shows significant potential as an automated decision-support tool for clinical lung cancer screening.