Deep Learning Approach to Diabetic Retinopathy Detection

TL;DR

This paper introduces a deep learning method using CNNs for early detection of diabetic retinopathy from fundus images, achieving high sensitivity and specificity, and addressing dataset limitations with a multistage transfer learning approach.

Contribution

It presents a novel deep learning approach with multistage transfer learning for diabetic retinopathy stage detection from fundus images, improving accuracy and robustness.

Findings

Sensitivity and specificity of 0.99 achieved

Ranked 54th out of 2943 methods on APTOS dataset

Quadratic weighted kappa score of 0.925466

Abstract

Diabetic retinopathy is one of the most threatening complications of diabetes that leads to permanent blindness if left untreated. One of the essential challenges is early detection, which is very important for treatment success. Unfortunately, the exact identification of the diabetic retinopathy stage is notoriously tricky and requires expert human interpretation of fundus images. Simplification of the detection step is crucial and can help millions of people. Convolutional neural networks (CNN) have been successfully applied in many adjacent subjects, and for diagnosis of diabetic retinopathy itself. However, the high cost of big labeled datasets, as well as inconsistency between different doctors, impede the performance of these methods. In this paper, we propose an automatic deep-learning-based method for stage detection of diabetic retinopathy by single photography of the human fundus. Additionally, we propose the multistage approach to transfer learning, which makes use of similar datasets with different labeling. The presented method can be used as a screening method for early detection of diabetic retinopathy with sensitivity and specificity of 0.99 and is ranked 54 of 2943 competing methods (quadratic weighted kappa score of 0.925466) on APTOS 2019 Blindness Detection Dataset (13000 images).