An investigation of deep learning algorithms applied to automated diagnosis for diabetic retinopathy

Cuong Do; Lan Vu

doi:10.1117/12.2579608

8 November 2020 An investigation of deep learning algorithms applied to automated diagnosis for diabetic retinopathy

Cuong Do, Lan Vu

Proceedings Volume 11525, SPIE Future Sensing Technologies; 115251Y (2020) https://doi.org/10.1117/12.2579608
Event: SPIE Future Sensing Technologies, 2020, Online Only

Conference Poster

Abstract

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.

Citation Download Citation

Cuong Do and 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

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