A Deep Learning Approach using EfficientNet-based Transfer Learning for Diabetic Retinopathy Diagnosis

Abstract

Detecting and treating diabetic retinopathy early is crucial to preventing blindness among the working-age population worldwide. The aim of the present study is to evaluate the efficacy of deep learning models in diagnosing diabetic retinopathy. The EfficientNets, which are claimed to be more efficient than many current well-performing network architectures, were tested to see how transfer learning performed with color fundus photography as input.