A novel microaneurysms detection approach based on convolutional neural networks with reinforcement sample learning algorithm
Author:
Funder
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu
Publisher
Springer Science and Business Media LLC
Subject
General Medicine
Link
http://link.springer.com/article/10.1007/s13755-017-0034-9/fulltext.html
Reference20 articles.
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2. Niemeijer M, van Ginneken B, Cree M, Mizutani A, Quellec G, Sanchez C, et al. Retinopathy online challenge: automatic detection of microaneurysms in digital color fundus photographs. IEEE Trans Med Imaging. 2010;29(1):185–95.
3. Antal B, Hajdu A. Improving microaneurysm detection using an optimally selected subset of candidate extractors and preprocessing methods. Pattern Recogn. 2012;45(1):264–70. https://doi.org/10.1016/j.patcog.2011.06.010 .
4. Antal B, Hajdu A. Improving microaneurysm detection in color fundus images by using context-aware approaches. Comput Med Imaging Graph. 2013;37:403–8. https://doi.org/10.1016/j.compmedimag.2013.05.001 .
5. Fleming AD, Philip S, Goatman KA, Olson JA, Sharp PF. Automated microaneurysm detection using local contrast normalization and local vessel detection. IEEE Trans Med Imaging. 2006;25(9):1223–32.
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