Farooq, S. and Rizwan, A. (2022) Glaucoma Detection and Classification Using Improved U-Net Deep Learning Model. has been cited by the following article: TITLE: Evaluation of Thermal Response of the ...

A deep learning algorithm displayed comparable or superior performance to human clinicians at detecting referable glaucoma using fundus photographs from a municipal teleretinal screening program, ...

Glaucoma diagnosis traditionally relies on a combination of fundus imaging, optical coherence tomography (OCT), intraocular pressure (IOP) measurements, and visual field (VF) testing. However, ...

Results demonstrate improved model interpretability and robust DR detection, highlighting deep learning’s potential for clinical use and suggesting future directions, such as integration with ...

Furthermore, deep learning algorithms trained with optical coherence tomography (OCT) data can detect microstructural damage due to glaucoma and its progression over time.

In the mild cataracts group (1,050 images), 11 images (1.05%) were misclassified as non-cataracts by the system due to clarity of the fundus images, most of the patients are early cortical or nuclear ...

A deep learning model may predict the probability of glaucomatous damage using retinal nerve fiber layer (RNFL) thickness obtained from spectral domain-optical coherence tomography (SD-OCT), according ...

The overall architecture of the ResNet consists of 16 residual blocks and each residual block consists of ... Classification of cataract fundus image based on deep learning. In: 2017 IEEE ...

Deep learning for glaucoma detection. Oct 31, 2018. Detecting glaucoma early. May 14, 2021. ... Using deep learning to predict the vision of glaucoma patients. Your friend's email. Your email.

Automated deep learning analysis of fundus photographs showed high diagnostic accuracy in determining primary open-angle glaucoma, with increased ability to detect glaucoma earlier than human ...