An enhanced diabetic retinopathy blindness detection using deep learning and image preprocessing techniques

Abstract

Diabetic retinopathy (DR) is a gradual and severe consequence of diabetes that increases the risk of visual impairment and blindness globally. Early and precise identification of DR is crucial for successful care, but availability to specialist diagnostic tools is restricted, especially in low-resource settings. This study aims to improve the automated identification of diabetic retinopathy using cutting-edge deep learning models—specifically ViT, CLIP, BLIP, ViLT, Florence-2, and ResNet50—which are applied to the APTOS Diabetic Retinopathy dataset. Diabetic Retinopathy (DR) diagnosis is often hindered by poor image quality and subtle differences across varying severity levels. The grading of DR is typically categorized into five stages: No DR, Mild, Moderate, Severe, and Proliferative, as defined by the International Clinical Diabetic Retinopathy Disease Severity Scale. To address artifacts such as uneven illumination, we employ Gaussian preprocessing to smooth noise and Wavelet transforms to enhance edge details. This study evaluates the impact of Gaussian and Wavelet preprocessing techniques on the performance of Vision-Language Models (e.g., CLIP, BLIP) and CNNs (e.g., RESNET50). Results indicate that Gaussian Filtering significantly enhances classification accuracy, yielding a 37% improvement for RESNET50 and 36% for BLIP compared to the baseline. This study demonstrates that appropriate preprocessing is critical for maximizing the potential of deep learning in medical imaging. Specifically, Gaussian Filtering proved superior to Wavelet techniques, offering substantial accuracy gains for models like BLIP and RESNET50. Future work will explore hybrid preprocessing pipelines to further distinguish between the subtle early stages of the disease.