Transfer-Ensemble Learning based Deep Convolutional Neural Networks for Diabetic Retinopathy Classification

TL;DR

This paper presents an ensemble deep learning approach combining VGG16 and Inception V3 to classify diabetic retinopathy into five stages with high accuracy, leveraging pre-trained models and feature concatenation.

Contribution

The study introduces a novel ensemble model that combines two pre-trained CNNs with frozen layers and feature concatenation for improved DR classification performance.

Findings

Achieved 96.4% accuracy on the DR classification task.

Effective use of ensemble of pre-trained CNNs enhances performance.

Demonstrated the model's robustness on the APTOS dataset.

Abstract

This article aims to classify diabetic retinopathy (DR) disease into five different classes using an ensemble approach based on two popular pre-trained convolutional neural networks: VGG16 and Inception V3. The proposed model aims to leverage the strengths of the two individual nets to enhance the classification performance for diabetic retinopathy. The ensemble model architecture involves freezing a portion of the layers in each pre-trained model to utilize their learned representations effectively. Global average pooling layers are added to transform the output feature maps into fixed-length vectors. These vectors are then concatenated to form a consolidated representation of the input image. The ensemble model is trained using a dataset of diabetic retinopathy images (APTOS), divided into training and validation sets. During the training process, the model learns to classify the retinal images into the corresponding diabetic retinopathy classes. Experimental results on the test set demonstrate the efficacy of the proposed ensemble model for DR classification achieving an accuracy of 96.4%.