PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
Diabetic retinopathy is one of the leading causes of blindness among the working age population world-wide due to late detection and intervention. This study pilots the application of deep learning models to automatically diagnose the occurrence and severity of diabetic retinopathy. With color fundus photography as input, this study tested the performance of transfer learning based on the most recent architectures of Convolutional Neural Network (CNN) models, the EfficientNets, claimed to be superior than many current well-performing network architectures.
Cuong Do andLan Vu
"An investigation of deep learning algorithms applied to automated diagnosis for diabetic retinopathy", Proc. SPIE 11525, SPIE Future Sensing Technologies, 115251Y (8 November 2020); https://doi.org/10.1117/12.2579608
ACCESS THE FULL ARTICLE
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
The alert did not successfully save. Please try again later.
Cuong Do, Lan Vu, "An investigation of deep learning algorithms applied to automated diagnosis for diabetic retinopathy," Proc. SPIE 11525, SPIE Future Sensing Technologies, 115251Y (8 November 2020); https://doi.org/10.1117/12.2579608