Deep learning (DL) has the potential to transform medical diagnostics. However, the
diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of …

The exponential increase in the number of diabetics around the world has led to an equally
large increase in the number of diabetic retinopathy (DR) cases which is one of the major …

Diabetic Retinopathy (DR) is a leading cause of vision loss in the world. In the past few
years, artificial intelligence (AI) based approaches have been used to detect and grade DR …

As the prevalence of diabetes increases, millions of people need to be screened for diabetic
retinopathy (DR). Remarkable advances in technology have made it possible to use artificial …

Retinal disease classification is a significant problem in computer-aided diagnosis (CAD) for
medical applications. This paper is focused on a 4-class classification problem to …

Diabetic retinopathy (DR) is a major health problem that can lead to vision loss if not treated
early. In this study, a three-step system for DR detection utilizing optical coherence …

Background and objectives Diabetic retinopathy (DR) is the leading cause of blindness
worldwide, and therefore its early detection is important in order to reduce disease-related …

Recently, artificial intelligence (AI) has been one of the hottest topics in the technological
world. Although it is involved in many domains, it was recently involved in the healthcare …

Purpose: Reliable classification of referable and vision threatening diabetic retinopathy (DR)
is essential for patients with diabetes to prevent blindness. Optical coherence tomography …

Purpose: To achieve automatic diabetic retinopathy (DR) detection in retinal fundus
photographs through the use of a deep transfer learning approach using the Inception-v3 …