Dual Branch Deep Learning Network for Detection and Stage Grading of Diabetic Retinopathy

TL;DR

This paper presents a dual-branch deep learning model that uses transfer learning for accurate detection and staging of diabetic retinopathy from fundus images, outperforming existing methods on a large multi-center dataset.

Contribution

The paper introduces a novel dual-branch deep learning network utilizing transfer learning for simultaneous detection and stage grading of diabetic retinopathy, trained on a large multi-center dataset.

Findings

Achieved 98.50% accuracy in binary detection

Attained 93.00 quadratic weighted kappa in stage grading

Outperformed existing literature in diabetic retinopathy classification

Abstract

Diabetic retinopathy is a severe complication of diabetes that can lead to permanent blindness if not treated promptly. Early and accurate diagnosis of the disease is essential for successful treatment. This paper introduces a deep learning method for the detection and stage grading of diabetic retinopathy, using a single fundus retinal image. Our model utilizes transfer learning, employing two state-of-the-art pre-trained models as feature extractors and fine-tuning them on a new dataset. The proposed model is trained on a large multi-center dataset, including the APTOS 2019 dataset, obtained from publicly available sources. It achieves remarkable performance in diabetic retinopathy detection and stage classification on the APTOS 2019, outperforming the established literature. For binary classification, the proposed approach achieves an accuracy of 98.50, a sensitivity of 99.46, and a specificity of 97.51. In stage grading, it achieves a quadratic weighted kappa of 93.00, an accuracy of 89.60, a sensitivity of 89.60, and a specificity of 97.72. The proposed approach serves as a reliable screening and stage grading tool for diabetic retinopathy, offering significant potential to enhance clinical decision-making and patient care.