Blindness (Diabetic Retinopathy) Severity Scale Detection

TL;DR

This paper introduces a deep learning approach combining vessel segmentation and dual CNN classifiers to automatically detect and classify diabetic retinopathy severity from retinal images, achieving high accuracy.

Contribution

A novel dual-path neural network method that integrates vessel segmentation with CNN classifiers for improved DR severity detection.

Findings

Achieved 94.80% accuracy in DR severity classification.

Outperformed many state-of-the-art methods on public datasets.

Attained a QWK score of 0.9254, indicating strong agreement.

Abstract

Diabetic retinopathy (DR) is a severe complication of diabetes that can cause permanent blindness. Timely diagnosis and treatment of DR are critical to avoid total loss of vision. Manual diagnosis is time consuming and error-prone. In this paper, we propose a novel deep learning based method for automatic screening of retinal fundus images to detect and classify DR based on the severity. The method uses a dual-path configuration of deep neural networks to achieve the objective. In the first step, a modified UNet++ based retinal vessel segmentation is used to create a fundus image that emphasises elements like haemorrhages, cotton wool spots, and exudates that are vital to identify the DR stages. Subsequently, two convolutional neural networks (CNN) classifiers take the original image and the newly created fundus image respectively as inputs and identify the severity of DR on a scale of 0 to 4. These two scores are then passed through a shallow neural network classifier (ANN) to predict the final DR stage. The public datasets STARE, DRIVE, CHASE DB1, and APTOS are used for training and evaluation. Our method achieves an accuracy of 94.80% and Quadratic Weighted Kappa (QWK) score of 0.9254, and outperform many state-of-the-art methods.