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Comparing humans and deep learning performance for grading AMD: A study in using universal deep features and transfer

Philippe Burlina¹, Katia D Pacheco², Neil Joshi³

¹Applied Physics Laboratory, The Johns Hopkins University, MD, USA; Retina Division, Wilmer Eye Institute, Johns Hopkins University School of Medicine, USA; Department of Computer Science, The Johns Hopkins University, MD, USA.

Computers in Biology and Medicine

|February 8, 2017

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Deep learning accurately identifies intermediate age-related macular degeneration (AMD) from retinal images, aiding early detection and treatment. This machine grading shows performance comparable to physicians, offering a promising tool for AMD management.

Failed At:

2026-06-19T13:37:41.176743+00:00

Keywords:

Age-related macular degeneration, (AMD)Deep Convolutional Neural Networks, (DCNNs)Deep learning Retinal image analysis Transfer learning Universal features

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