Acceleration of convolutional neural network based diabetic retinopathy diagnosis system on field programmable gate array

Diabetic retinopathy (DR) is one of the most common causes of blindness. The necessity for a robust and automated DR screening system for regular examination has long been recognized in order to identify DR at an early stage. In this paper, an embedded DR diagnosis system based on convolutional neural networks (CNNs) has been proposed to assess the proper stage of DR. We coupled the power of CNN with transfer learning to design our model based on state-of-the-art architecture. We preprocessed the input data, which is color fundus photography, to reduce undesirable noise in the image. After training many models on the dataset, we chose the adopted ResNet50 because it produced the best results, with a 92.90% accuracy. Extensive experiments and comparisons with other research work show that the proposed method is effective. Furthermore, the CNN model has been implemented on an embedded target to be a part of a medical instrument diagnostic system. We have accelerated our model inference on a field programmable gate array (FPGA) using Xilinx tools. Results have confirmed that a customized FPGA system on chip (SoC) with hardware accelerators is a promising target for our DR detection model with high performance and low power consumption.