An Improved Model for Diabetic Retinopathy Detection by using Transfer Learning and Ensemble Learning

TL;DR

This paper presents an enhanced diabetic retinopathy detection model using transfer learning with multiple pre-trained CNN architectures and ensemble techniques, achieving near-perfect accuracy and metrics.

Contribution

It introduces a novel ensemble approach combining DenseNet architectures with transfer learning for highly accurate DR detection.

Findings

DenseNet121 achieved 100% accuracy.

Ensemble of DenseNet169 and DenseNet201 also achieved 100% accuracy.

The model significantly outperforms previous methods in accuracy and metrics.

Abstract

Diabetic Retinopathy (DR) is an ocular condition caused by a sustained high level of sugar in the blood, which causes the retinal capillaries to block and bleed, causing retinal tissue damage. It usually results in blindness. Early detection can help in lowering the risk of DR and its severity. The robust and accurate prediction and detection of diabetic retinopathy is a challenging task. This paper develops a machine learning model for detecting Diabetic Retinopathy that is entirely accurate. Pre-trained models such as ResNet50, InceptionV3, Xception, DenseNet121, VGG19, NASNetMobile, MobileNetV2, DensNet169, and DenseNet201 with pooling layer, dense layer, and appropriate dropout layer at the bottom of them were carried out in transfer learning (TL) approach. Data augmentation and regularization was performed to reduce overfitting. Transfer Learning model of DenseNet121, Average and weighted ensemble of DenseNet169 and DenseNet201 TL architectures contribute individually the highest accuracy of 100%, the highest precision, recall, F-1 score of 100%, 100%, and 100%, respectively.