Low-Rank Adaptation Applied to Multiclass Diabetic Retinopathy Classification

Diabetic retinopathy is an eye complication caused by a widespread disease named diabetes mellitus. The examination of retinal fundus images procured by retinography is the most commonly used method for diagnosing diabetic retinopathy. Strategies based on deep learning have shown promising results in detecting diabetic retinopathy, achieving performance similar to that of the human eye regarding image inspection. However, the performance of these strategies heavily depends on fine-tuning the algorithm hyper-parameters and big datasets. In this work, we propose training a Deep Learning model with Low-Rank Adaptation (LoRA) approach to classify three stages of Diabetic Retinopathy: i) no sign of diabetic retinopathy, ii) Non-proliferative diabetic retinopathy, and iii) proliferative diabetic retinopathy. We propose using a low-rank representation to reduce significantly the number of trainable parameters. The experiment shows that the LoRA approach for image classification of the three stages of diabetic retinopathy manages to obtain state-of-the-art results even with a small dataset.