Enhancing Diabetic Retinopathy Detection with CNN-Based Models: A Comparative Study of UNET and Stacked UNET Architectures

TL;DR

This paper compares UNET and Stacked UNET CNN architectures for diabetic retinopathy detection, achieving over 92% accuracy using retinal images, addressing challenges like data scarcity and image variability.

Contribution

It demonstrates the effectiveness of UNET and Stacked UNET models in classifying diabetic retinopathy stages with high accuracy on a public dataset.

Findings

UNET achieved 92.81% accuracy

Stacked UNET achieved 93.32% accuracy

Both models effectively classify DR severity levels

Abstract

Diabetic Retinopathy DR is a severe complication of diabetes. Damaged or abnormal blood vessels can cause loss of vision. The need for massive screening of a large population of diabetic patients has generated an interest in a computer-aided fully automatic diagnosis of DR. In the realm of Deep learning frameworks, particularly convolutional neural networks CNNs, have shown great interest and promise in detecting DR by analyzing retinal images. However, several challenges have been faced in the application of deep learning in this domain. High-quality, annotated datasets are scarce, and the variations in image quality and class imbalances pose significant hurdles in developing a dependable model. In this paper, we demonstrate the proficiency of two Convolutional Neural Networks CNNs based models, UNET and Stacked UNET utilizing the APTOS Asia Pacific Tele-Ophthalmology Society Dataset. This system achieves an accuracy of 92.81% for the UNET and 93.32% for the stacked UNET architecture. The architecture classifies the images into five categories ranging from 0 to 4, where 0 is no DR and 4 is proliferative DR.