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Erschienen in:
Anti-occlusion Light-Field Optical Flow Estimation Using Light-Field Super-Pixels Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Enhanced Detection of Referable Diabetic Retinopathy via DCNNs and Transfer Learning

verfasst von : Michelle Yuen Ting Yip, Zhan Wei Lim, Gilbert Lim, Nguyen Duc Quang, Haslina Hamzah, Jinyi Ho, Valentina Bellemo, Yuchen Xie, Xin Qi Lee, Mong Li Lee, Wynne Hsu, Tien Yin Wong, Daniel Shu Wei Ting

Erschienen in: Computer Vision – ACCV 2018 Workshops

Verlag: Springer International Publishing

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Abstract

A clinically acceptable deep learning system (DLS) has been developed for the detection of diabetic retinopathy by the Singapore Eye Research Institute. For its utility in a national screening programme, further enhancement was needed. With newer deep convolutional neural networks (DCNNs) being introduced and technological methodology such as transfer learning gaining recognition for better performance, this paper compared the performance of the DCNN used in the original DLS, VGGNet, with newer DCNNs, ResNet and Ensemble, with transfer learning. The DLS performance improved with higher AUC, sensitivity and specificity with the adoption of the newer DCNNs and transfer learning.

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Vorheriges Kapitel Intermediate Goals in Deep Learning for Retinal Image Analysis
Nächstes Kapitel Generative Adversarial Networks (GANs) for Retinal Fundus Image Synthesis
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Metadaten
Titel
Enhanced Detection of Referable Diabetic Retinopathy via DCNNs and Transfer Learning
verfasst von
Michelle Yuen Ting Yip
Zhan Wei Lim
Gilbert Lim
Nguyen Duc Quang
Haslina Hamzah
Jinyi Ho
Valentina Bellemo
Yuchen Xie
Xin Qi Lee
Mong Li Lee
Wynne Hsu
Tien Yin Wong
Daniel Shu Wei Ting
Copyright-Jahr
2019
Buch
Computer Vision – ACCV 2018 Workshops

Print ISBN: 978-3-030-21073-1

Electronic ISBN: 978-3-030-21074-8

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-21074-8_23

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