Enhancing Cytology-Based Cancer Detection Using Diffusion-Driven Synthetic Data Augmentation

Cytology-based cancer detection, particularly in cer- vical cancer screening, plays a critical role in early diagnosis and reducing disease-related mortality. However, the performance of automated deep learning models in this domain is often constrained by the limited availability of high-quality annotated datasets and severe class imbalance, especially for rare pathologi- cal conditions such as high-grade lesions and carcinoma. Conven- tional data augmentation techniques, including geometric trans- formations and intensity variations, primarily introduce superfi- cial diversity and fail to capture the complex biological variations inherent in cytological structures. As a result, models trained using such augmentation strategies often exhibit poor generaliza- tion and reduced sensitivity toward clinically significant minority classes. To overcome these limitations, this study proposes a diffusion-driven synthetic data augmentation framework that leverages denoising diffusion probabilistic models (DDPMs) to generate high-fidelity cytology images. Unlike traditional aug- mentation methods, diffusion models learn the underlying data distribution and progressively reconstruct images from noise, enabling the generation of biologically meaningful variations that closely resemble real cellular patterns. The generated synthetic images are integrated with real cytology datasets to enhance both data diversity and class balance. The augmented dataset is used to train deep learning classification models, and extensive experiments are conducted to evaluate the effectiveness of the proposed approach. Results demonstrate that diffusion-driven augmentation significantly improves classification performance across multiple metrics, including accuracy, precision, recall, and F1-score. Notably, substantial improvements are observed in minority classes, indicating enhanced sensitivity and robustness. Furthermore, qualitative analysis confirms that the generated images preserve critical diagnostic features such as nuclear morphology and chromatin texture, making them suitable for clinical interpretation. The proposed framework highlights the potential of diffusion-based augmentation as a powerful tool for addressing data scarcity in medical imaging and improving the reliability of AI-driven diagnostic systems.