The use of fundus images for the early screening of eye diseases is of great clinical
importance. Due to its powerful performance, deep learning is becoming more and more …

Clinical trials consume the latter half of the 10 to 15 year, 1.5–2.0 billion USD, development
cycle for bringing a single new drug to market. Hence, a failed trial sinks not only the …

Deep learning algorithms, in particular convolutional networks, have rapidly become a
methodology of choice for analyzing medical images. This paper reviews the major deep …

Artificial intelligence is widely applied to automate Diabetic retinopathy diagnosis. Diabetes-
related retinal vascular disease is one of the world's most common leading causes of …

Diabetic retinopathy (DR) is the most common cause of blindness in middle-aged people. It
shows that an automatic image evaluation system is needed in the diagnosis of this disease …

Glaucoma detection in color fundus images is a challenging task that requires expertise and
years of practice. In this study we exploited the application of different Convolutional Neural …

Training robust deep learning (DL) systems for medical image classification or segmentation
is challenging due to limited images covering different disease types and severity. We …

Background In the domain of natural language processing, Transformers are recognized as
state‐of‐the‐art models, which opposing to typical convolutional neural networks (CNNs) do …

Diabetic Retinopathy (DR) represents a highly-prevalent complication of diabetes in which
individuals suffer from damage to the blood vessels in the retina. The disease manifests …

Early detection and proper screening are essential to prevent vision loss due to glaucoma.
In recent years, convolutional neural network (CNN) has been successfully applied to the …