Diabetic Retinopathy Detection Using Transfer Learning and Deep Learning

摘要

Diabetic retinopathy is one of the major causes of blindness in the population aged 20-65. In this paper, we address the problem of automatic diabetic retinopathy detection and proposed a novel deep learning hybrid to solve the problem. We use transfer learning on pre-trained Inception-ResNet-v2 and added a custom block of CNN layers on top of Inception-ResNet-v2 for building the hybrid model. We evaluated the performance of the proposed model on Messidor-1 diabetic retinopathy dataset and APTOS 2019 blindness detection (Kaggle dataset). Our model performed better than other published results. We achieved a test accuracy of 72.33% and 82.18% on Messidor-1 and APTOS dataset, respectively.