The simultaneous maturation of multiple digital and telecommunications technologies in
2020 has created an unprecedented opportunity for ophthalmology to adapt to new models …

Objective Employing transfer learning (TL) with convolutional neural networks (CNNs), well-
trained on non-medical ImageNet dataset, has shown promising results for medical image …

Purpose To assess the performance of a deep learning algorithm for detecting referable
glaucomatous optic neuropathy (GON) based on color fundus photographs. Design A deep …

The ability of deep learning architectures to identify glaucomatous optic neuropathy (GON)
in fundus photographs was evaluated. A large database of fundus photographs (n= 14,822) …

Importance A deep learning system (DLS) that could automatically detect glaucomatous
optic neuropathy (GON) with high sensitivity and specificity could expedite screening for …

Glaucoma is one of the leading causes of irreversible vision loss. Many approaches have
recently been proposed for automatic glaucoma detection based on fundus images …

Recently, the attention mechanism has been successfully applied in convolutional neural
networks (CNNs), significantly boosting the performance of many computer vision tasks …

Purpose To develop and evaluate deep learning models that screen multiple abnormal
findings in retinal fundus images. Design Cross-sectional study. Participants For the …

Glaucoma is one of the eye diseases stimulated by the fluid pressure that increases in the
eyes, damaging the optic nerves and causing partial or complete vision loss. As Glaucoma …

Background With the advancement of powerful image processing and machine learning
techniques, Computer Aided Diagnosis has become ever more prevalent in all fields of …