Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods 2020
DOI: 10.5220/0008970805010509
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Deep Learning Approach to Diabetic Retinopathy Detection

Abstract: Diabetic retinopathy is one of the most threatening complications of diabetes that leads to permanent blindness if left untreated. One of the essential challenges is early detection, which is very important for treatment success. Unfortunately, the exact identification of the diabetic retinopathy stage is notoriously tricky and requires expert human interpretation of fundus images. Simplification of the detection step is crucial and can help millions of people. Convolutional neural networks (CNN) have been suc… Show more

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Cited by 116 publications
(30 citation statements)
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“…The phases of diabetic retinopathy are with four stages, including micro aneurysms, hemorrhage, neovascularization, and venous leading. The four stages denote the ponderances of retinopathy [37]:…”
Section: A Diabetic Retinopathy Diagnosismentioning
confidence: 99%
“…The phases of diabetic retinopathy are with four stages, including micro aneurysms, hemorrhage, neovascularization, and venous leading. The four stages denote the ponderances of retinopathy [37]:…”
Section: A Diabetic Retinopathy Diagnosismentioning
confidence: 99%
“…To further demonstrate the advanced DR grading performance, we compare our method with three completely supervised methods, including SE-ResNeXt50 [31], EfficientNet [32], and EnsembleNet [33], which are proposed recently with same training and testing data. In detail, SE-ResNeXt50 [31] designed a squeeze-and-excitation (SE) block adaptively recalibrating channel-wise feature responses by explicitly modeling interdependencies between channels, which boost the representational power of a network.…”
Section: E Comparison With Supervised Modelsmentioning
confidence: 99%
“…In [20] they introduce a Diabetic Retinopathy Detection Deep Learning Approach. We propose an AI-based methodology for stage detection of DR in this analysis using single imagery of the person fundus.…”
Section: In [5] They Have Explored Implementing Machinementioning
confidence: 99%