Background: As deep learning becomes increasingly accessible for automated detection of
diabetic retinopathy (DR), questions persist regarding its performance equity among diverse …

Diabetic retinopathy (DR) is one of the most common eye conditions among diabetic
patients. However, vision loss occurs primarily in the late stages of DR, and the symptoms of …

Purpose: Prior studies report impressive diabetic retinopathy (DR) detection performance
using deep learning systems (DLS). However, the utility of DLS for predicting DR and …

Diabetic retinopathy (DR), a critical complication of diabetes, significantly increases the risk
of vision loss. Prompt detection is essential for effective intervention, yet existing diagnostic …

Deep learning (DL) has been shown to be effective in developing diabetic retinopathy (DR)
algorithms, possibly tackling financial and manpower challenges hindering implementation …

Diabetic retinopathy (DR) is a late microvascular complication of Diabetes Mellitus (DM) that
could lead to permanent blindness in patients, without early detection. Although adequate …

Background: Diabetic retinopathy (DR) is a frequent concomitant disease of diabetes,
affecting millions worldwide. Screening for this disease based on fundus images has been …

Diabetic retinopathy (DR), a potentially vision-threatening condition, necessitates accurate
diagnosis and staging, which deep-learning models can facilitate. However, in clinical …

Diabetic retinopathy (DR) is one of the fastest growing causes of blindness and has
prompted the implementation of national screening programs. To help address the shortage …

Diabetic retinopathy (DR) is the most important complication of diabetes. Early diagnosis by
performing retinal image analysis helps avoid visual loss or blindness. A computer-aided …