A deep learning model for classification of diabetic retinopathy in eye fundus images based on retinal lesion detection

TL;DR

This paper introduces a deep learning model that automatically classifies diabetic retinopathy from eye fundus images by detecting retinal lesions, aiming to replicate clinical diagnosis and improve efficiency.

Contribution

It presents a novel model that mimics clinical workflow by identifying ocular lesions for DR classification, and provides publicly available datasets with lesion annotations.

Findings

Model achieves AUC of 0.948 for DR diagnosis.

Sensitivity of 0.886 and specificity of 0.875 in classification.

Uses publicly available datasets for training and testing.

Abstract

Diabetic retinopathy (DR) is the result of a complication of diabetes affecting the retina. It can cause blindness, if left undiagnosed and untreated. An ophthalmologist performs the diagnosis by screening each patient and analyzing the retinal lesions via ocular imaging. In practice, such analysis is time-consuming and cumbersome to perform. This paper presents a model for automatic DR classification on eye fundus images. The approach identifies the main ocular lesions related to DR and subsequently diagnoses the illness. The proposed method follows the same workflow as the clinicians, providing information that can be interpreted clinically to support the prediction. A subset of the kaggle EyePACS and the Messidor-2 datasets, labeled with ocular lesions, is made publicly available. The kaggle EyePACS subset is used as a training set and the Messidor-2 as a test set for lesions and DR classification models. For DR diagnosis, our model has an area-under-the-curve, sensitivity, and specificity of 0.948, 0.886, and 0.875, respectively, which competes with state-of-the-art approaches.