The aim of this systematic literature review (SLR) is to identify and critically evaluate current
research advancements with respect to small data and the use of data augmentation …

Designing deep learning based methods with medical images has always been an attractive
area of research to assist clinicians in rapid examination and accurate diagnosis. Those …

Diabetes can cause damage to the retina's blood vessels in the eye leading to diabetic
retinopathy (DR). The images captured using a fundus camera are used to segment and …

The Proposed work intends to automate the detection and classification of diabetic
retinopathy from retinal fundus image which is very important in ophthalmology. Most of the …

Retinopathy is the primary cause of irreversible yet preventable blindness. Numerous deep-
learning algorithms have been developed for automatic retinal fundus image analysis …

Objective To assess the performance of convolutional neural networks (CNNs) for
automated diagnosis of dry eye (DE) in patients undergoing video keratoscopy based on …

Diabetic Retinopathy (DR) is one the most important problems of diabetics and it directs to
the main cause of blindness. When proper treatment is afforded for DR patients, almost 90 …

Objective To assess the performance of convolutional neural networks (CNNs) for
automated detection of keratoconus (KC) in standalone Scheimpflug-based dynamic …

A deep learning-based convolutional artificial neural networks structured a new image
classification method approach was implemented in the study. Sample application was …

Diabetic retinopathy (DR) and diabetic macular edema (DME) are forms of eye illness
caused by diabetes that affects the blood vessels in the eyes, with the ground occupied by …