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Diffusion models vs. DCGANs for class-imbalanced lung cancer CT classification: A comparative study.

Masoud Tabibian1, Tahereh Razmpour1, Rajib Saha1

  • 1Department of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States of America.

Intelligence-Based Medicine
|April 1, 2026
PubMed
Summary
This summary is machine-generated.

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Diffusion models outperform DCGANs in addressing class imbalance for lung cancer CT classification, offering superior recall and consistency for improved cancer screening accuracy.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Machine Learning

Background:

  • Class imbalance in lung cancer CT scans leads to biased models and missed diagnoses.
  • Benign and normal cases are often underrepresented, impacting screening sensitivity.

Purpose of the Study:

  • To compare Diffusion Models and Deep Convolutional Generative Adversarial Networks (DCGANs) for addressing class imbalance in lung cancer CT classification.
  • To evaluate generative approaches using image quality metrics and downstream classification performance.

Main Methods:

  • Comparative analysis of Diffusion Models and DCGANs with spectral normalization, self-attention, and conditional generation.
  • Utilized the IQ-OTH/NCCD dataset (1097 CT images) with 10 independent runs for validation.
  • Evaluated using Frechet Inception Distance, KL Divergence, Kernel Inception Distance, Inception Score, and classification accuracy.
Keywords:
CT scansClass imbalanceDCGANDeep learningDiffusion modelsLung cancerMedical imagingSynthetic data

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Main Results:

  • Diffusion models showed superior image quality metrics and downstream classification performance compared to DCGANs.
  • Both methods improved benign recall; Diffusion models achieved perfect benign recall (1.000 ± 0.000) and higher overall accuracy (0.9959 ± 0.0068).
  • Diffusion models demonstrated higher malignant detection sensitivity (0.997 ± 0.008) with lower performance variance.

Conclusions:

  • Diffusion models are the preferred approach for high-stakes clinical applications like cancer screening due to superior recall and consistency.
  • Downstream clinical task performance is critical for validating generative models, not just image quality metrics.
  • Both Diffusion Models and DCGANs can mitigate class imbalance, but Diffusion Models offer enhanced reliability for medical diagnosis.