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An efficient hybrid artificial intelligence framework for lung cancer classification using CT images.

L Kamala1, K G Mohan2

  • 1Department of Computer Science and Engineering, GITAM School of Computer Science and Engineering, GITAM (Deemed to be university), Bengaluru, 561203, India. lllkamala5@gmail.com.

Scientific Reports
|December 24, 2025
PubMed
Summary

A new hybrid Artificial Intelligence (AI) model accurately predicts lung cancer from CT scans. This AI approach improves early detection, enhancing patient survival rates for this dangerous disease.

Keywords:
AccuracyAugmentationComputed tomography imagesFeature extractionLung cancerMobileNetVisual geometry group-16

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Lung cancer has a low survival rate, necessitating early detection for improved patient outcomes.
  • Computed Tomography (CT) imaging aids lung disease identification but manual analysis is time-consuming and prone to errors.
  • Automated lung cancer prediction using AI can overcome limitations of manual CT image analysis.

Purpose of the Study:

  • To develop and evaluate a hybrid AI model for classifying lung CT images as normal, benign, or malignant.
  • To compare the performance of different feature extraction methods combined with deep learning models for lung cancer prediction.

Main Methods:

  • A hybrid AI model was proposed, integrating traditional features (Gray-Level Co-occurrence Matrix, Scale-Invariant Feature Transform) with Deep Learning features (VGG-16, MobileNet).
  • Features were extracted from lung CT images in the Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases (IQ-OTH/NCCD) dataset, pre-processed, and fused.
  • Six combinations of feature extraction modules were evaluated for classification performance.

Main Results:

  • The integration of Gray-Level Co-occurrence Matrix (GLCM) and Scale-Invariant Feature Transform (SIFT) with MobileNet achieved the highest accuracy, precision, recall, F1-score, and specificity.
  • The proposed hybrid AI model outperformed existing state-of-the-art methods in lung cancer prediction.
  • The model demonstrated reliable performance and suitability for real-time deployment.

Conclusions:

  • The hybrid AI model combining traditional and deep learning features offers a reliable and accurate method for lung cancer prediction from CT images.
  • This AI-driven approach enhances early lung cancer detection, potentially increasing survival rates.
  • The developed model shows promise for clinical application and real-time deployment in healthcare settings.