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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, Nebraska, United States of America.

Biorxiv : the Preprint Server for Biology

|December 3, 2025

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Diffusion models outperform DCGANs in addressing class imbalance for lung cancer CT detection. Diffusion models offer superior recall and consistency, crucial for accurate cancer screening and reducing missed diagnoses.

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, challenging accurate detection.

Purpose of the Study:

Compare Diffusion Models and Deep Convolutional Generative Adversarial Networks (DCGANs) for lung cancer CT classification.
Evaluate their effectiveness in addressing class imbalance using modern architectural enhancements.

Main Methods:

Utilized the IQ-OTH/NCCD dataset (1,097 CT images).
Incorporated spectral normalization, self-attention, and conditional generation in both models.
Evaluated using image quality metrics (FID, KL, KID, IS) and classification performance.

Keywords:

CT scans Class imbalance DCGAN Deep learning Diffusion models Lung cancer Medical imaging Synthetic data

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Main Results:

Diffusion models showed superior image quality and downstream classification performance.
Both models improved benign recall; Diffusion achieved perfect recall (1.000 ± 0.000).
Diffusion models maintained higher malignant detection sensitivity (0.997 ± 0.008) with lower variance.

Conclusions:

Diffusion models are the preferred approach for high-stakes clinical applications like cancer screening.
Downstream clinical task performance is critical for validation, surpassing image quality metrics alone.
Both generative approaches effectively mitigate class imbalance in medical imaging datasets.