Comparative Study of Fine-Tuning of Pre-Trained Convolutional Neural Networks for Diabetic Retinopathy Screening

Abstract

Diabetic retinopathy is the leading cause of blindness, engaging people in different ages. Early detection of the disease, although significantly important to control and cure it, is usually being overlooked due to the need for experienced examination. To this end, automatic diabetic retinopathy diagnostic methods are proposed to facilitate the examination process and act as the physician's helper. In this paper, automatic diagnosis of diabetic retinopathy using pre-trained convolutional neural networks is studied. Pre-trained networks are chosen to avoid the time-and resource-consuming training algorithms for designing a convolutional neural network from scratch. Each neural network is fine-tuned with the pre-processed dataset, and the fine-tuning parameters as well as the pre-trained neural networks are compared together. The result of this paper, introduces a fast approach to fine-tune pre-trained networks, by studying different tuning parameters and their effect on the overall system performance due to the specific application of diabetic retinopathy screening.