An Ensembled Method For Diabetic Retinopathy Classification using Transfer Learning

Diabetes affects 40-45% of Diabetic Retinopathy (DR) patients in the US. Early detection of DR may prevent or postpone vision deterioration, but it is difficult since the disorder often manifests with few symptoms until it is too late to treat. Clinically, DR is routinely treated using fundus images, with an estimated 200 million cases worldwide and over 400,000 deaths each year. A great deal of progress has been made by applying machine learning algorithms to the fundus images. As a result, image classification and detection have become reliable techniques for detecting the severity of diabetic retinopathy. Convolutional Neural Networks (CNNs) play a crucial role in the image classification and detection process by capturing various images' details, enabling a fast and efficient method for detecting diabetic retinopathy. CNN pre-trained models such as ResNet50, InceptionV3, and EfficientNetB7 have substantially improved their performance in the ImageNet Large-Scale Visual Recognition Competition. In addition, these pre-trained models are more precise and inexpensive to train because of the shorter connections between their input and output layers. This work proposes an approach for image classification that ensembles three pre-trained models, namely: EfficientNetB7, ResNet50V2, and InceptionV3, to perform the classification of the diabetic retinopathy subtypes. Our proposed method achieves 97.43% accuracy by adjusting the weights of the pre-trained models in detecting DR using the EyePacs Dataset.