Transfer-Ensemble Learning based Deep Convolutional Neural Networks for
  Diabetic Retinopathy Classification
Susmita Ghosh Abhiroop Chatterjee
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%.