A deep learning framework with edge computing for severity level detection of diabetic retinopathy

Diabetic retinopathy is one of the major causes of the vision loss worldwide. Its timely detection is critical for planning an efficient treatment process. Typically, fundus images are taken for diagnosis of diabetic retinopathy and determining its corresponding severity level. In this study, a framework that uses a mobile edge device for detecting the severity level of diabetic retinopathy is proposed. For this purpose, a dataset of fundus images containing five different diabetic retinopathy severity levels is utilized. The mobile device is responsible for performing the edge processing operations in which the fundus images are preprocessed by cropping, unsharp masking, and resizing. The preprocessed images are then transmitted to a cloud computing platform over the internet. In the cloud server, a concatenation ensemble deep learning models is trained for detecting the severity level of the diabetic retinopathy. The ensemble model involves three benchmark convolutional neural network architectures that are EfficientNetB7, ResNet50, and VGG19. The classification accuracy achieved using the concatenation ensemble is 96%, which is higher than those obtained via individual convolutional neural network models. In addition, contributions of edge computing are shown by calculating the total amount of transmitted data and the response time from the cloud server. It was observed that for classifying the entire test set 2984.52 Kb of less data, corresponding to average data size reduction of 85.2%, was transmitted and the response time was reduced by 6.14 seconds when the preprocessing steps are performed at the edge device.