Deep learning-based detection and classification of geographic atrophy using a deep convolutional neural network classifier.

Journal: Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie

Published Date: Aug 8, 2018

Abstract

PURPOSE: To automatically detect and classify geographic atrophy (GA) in fundus autofluorescence (FAF) images using a deep learning algorithm.

Authors

Maximilian Treder

Department of Ophthalmology, University of Muenster Medical Center, Domagkstraße 15, 48149, Muenster, Germany. maximilian.treder@ukmuenster.de.
Jost Lennart Lauermann

Department of Ophthalmology, University of Muenster Medical Center, Domagkstraße 15, 48149, Muenster, Germany.
Nicole Eter

Department of Ophthalmology, University of Muenster Medical Center, Domagkstraße 15, 48149, Muenster, Germany.

Keywords

Aged Algorithms Diagnosis, Computer-Assisted Disease Progression Female Fluorescein Angiography Fundus Oculi Geographic Atrophy Healthy Volunteers Humans Machine Learning Male Middle Aged Neural Networks, Computer Reproducibility of Results Sensitivity and Specificity Tomography, Optical Coherence

External Resources

View on PubMed Access via DOI PubMed (30091055)

Deep learning-based detection and classification of geographic atrophy using a deep convolutional neural network classifier.

Abstract

Authors

Keywords

External Resources

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