Abstract:
:Diabetes is a globally prevalent disease that can cause visible microvascular complications such as diabetic retinopathy and macular edema in the human eye retina, the images of which are today used for manual disease screening and diagnosis. This labor-intensive task could greatly benefit from automatic detection using deep learning technique. Here we present a deep learning system that identifies referable diabetic retinopathy comparably or better than presented in the previous studies, although we use only a small fraction of images (<1/4) in training but are aided with higher image resolutions. We also provide novel results for five different screening and clinical grading systems for diabetic retinopathy and macular edema classification, including state-of-the-art results for accurately classifying images according to clinical five-grade diabetic retinopathy and for the first time for the four-grade diabetic macular edema scales. These results suggest, that a deep learning system could increase the cost-effectiveness of screening and diagnosis, while attaining higher than recommended performance, and that the system could be applied in clinical examinations requiring finer grading.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Sahlsten J,Jaskari J,Kivinen J,Turunen L,Jaanio E,Hietala K,Kaski Kdoi
10.1038/s41598-019-47181-wsubject
Has Abstractpub_date
2019-07-24 00:00:00pages
10750issue
1issn
2045-2322pii
10.1038/s41598-019-47181-wjournal_volume
9pub_type
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