A Comparative Study of Filters and Deep Learning Models to predict Diabetic Retinopathy

TL;DR

This study compares various deep learning models and image filters to improve early detection and severity classification of Diabetic Retinopathy, achieving up to 96% accuracy with InceptionNetV3 and Gaussian filtering.

Contribution

It provides a comparative analysis of different CNN models and image filters, identifying the most effective combination for DR detection.

Findings

Gaussian filter with InceptionNetV3 achieves 96% accuracy.

Gabor filter's performance is less effective than Gaussian.

Deep learning models enhance early DR detection.

Abstract

The retina is an essential component of the visual system, and maintaining eyesight depends on the timely and accurate detection of disorders. The early-stage detection and severity classification of Diabetic Retinopathy (DR), a significant risk to the public's health is the primary goal of this work. This study compares the outcomes of various deep learning models, including InceptionNetV3, DenseNet121, and other CNN-based models, utilizing a variety of image filters, including Gaussian, grayscale, and Gabor. These models could detect subtle pathological alterations and use that information to estimate the risk of retinal illnesses. The objective is to improve the diagnostic processes for DR, the primary cause of diabetes-related blindness, by utilizing deep learning models. A comparative analysis between Greyscale, Gaussian and Gabor filters has been provided after applying these filters on the retinal images. The Gaussian filter has been identified as the most promising filter by resulting in 96% accuracy using InceptionNetV3.