Predictive Analysis of Diabetic Retinopathy with Transfer Learning

TL;DR

This study evaluates the effectiveness of transfer learning with CNN architectures like VGG16, ResNet50 V2, and EfficientNet B0 for early detection of Diabetic Retinopathy, achieving high accuracy in classification tasks.

Contribution

It compares multiple CNN architectures with transfer learning for DR detection, identifying VGG16 as the most effective model with 95% accuracy.

Findings

VGG16 achieved 95% accuracy in DR classification.

ResNet50 V2 closely followed with 93% accuracy.

Transfer learning significantly improves DR detection performance.

Abstract

With the prevalence of Diabetes, the Diabetes Mellitus Retinopathy (DR) is becoming a major health problem across the world. The long-term medical complications arising due to DR have a significant impact on the patient as well as the society, as the disease mostly affects individuals in their most productive years. Early detection and treatment can help reduce the extent of damage to the patients. The rise of Convolutional Neural Networks for predictive analysis in the medical field paves the way for a robust solution to DR detection. This paper studies the performance of several highly efficient and scalable CNN architectures for Diabetic Retinopathy Classification with the help of Transfer Learning. The research focuses on VGG16, Resnet50 V2 and EfficientNet B0 models. The classification performance is analyzed using several performance metrics including True Positive Rate, False Positive Rate, Accuracy, etc. Also, several performance graphs are plotted for visualizing the architecture performance including Confusion Matrix, ROC Curve, etc. The results indicate that Transfer Learning with ImageNet weights using VGG 16 model demonstrates the best classification performance with the best Accuracy of 95%. It is closely followed by ResNet50 V2 architecture with the best Accuracy of 93%. This paper shows that predictive analysis of DR from retinal images is achieved with Transfer Learning on Convolutional Neural Networks.