Citation

Alam, Touhidul and Liew, Tze Hui and Morol, Md. Kishor and Nandi, Dip and Jubair, Md. Abdullah- Al and Rahman, Mashiour (2026) Class balanced diabetic retinopathy image synthesis using a latent diffusion framework. Discover Artificial Intelligence, 6 (1). ISSN 2731-0809

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Abstract

Diabetic Retinopathy (DR) is one of the major causes of preventable blindness globally. Automated screening for DR is critical but is severely hindered by data scarcity and class imbalance. Real-world datasets, such as APTOS 2019, exhibit extreme class imbalance, where sight-threatening classes are statistically rare. Traditional augmentation fails to capture complex pathological features, while Generative Adversarial Networks (GANs) often suffer from mode collapse. Existing diffusion approaches typically operate in pixel space, limiting image resolution and requiring extreme computational resources. To address this, we introduce the Diabetic Retinopathy Latent Diffusion Synthesizer (DR-LDS), a class-conditional framework that synthesizes high-fidelity, 512 × 512 fundus images natively deployable on consumer-grade hardware. By leveraging a fine-tuned Variational Autoencoder (VAE) for domain-adapted latent space compression, alongside an optimized U-Net, our method achieves superior anatomical realism with convergence in just 150 epochs (at 17 min 22 s per epoch). Extensive benchmarking demonstrates that DR-LDS outperforms state-of-the-art baselines, achieving a Fréchet Inception Distance (FID) of 8.05. When used to augment minority classes to a uniform target for the APTOS 2019 dataset across 11 deep learning architectures, our synthetic data significantly improved diagnostic accuracy by up to 15.25% (

Item Type:	Article
Uncontrolled Keywords:	Medical image synthesis, Data augmentation, Class imbalance
Subjects:	R Medicine > R Medicine (General) > R858-859.7 Computer applications to medicine. Medical informatics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	01 Oct 2026 07:01
Last Modified:	01 Oct 2026 07:01
URII:	http://shdl.mmu.edu.my/id/eprint/16787

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