Detecting Diabetes Retinopathy Through Machine Learning
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-1682-1_12
Reference7 articles.
1. Acharya R, Chua CK, Ng EYK, Yu W and Chee C (2008). Application of higher order spectra for the identification of diabetes retinopathy stages. Journal of Medical Systems, 32(6):481–488.
2. Akhilesh Kumar Gangwar, Vadlamani Ravi (2020) Diabetic Retinopathy Detection Using Transfer Learning and Deep Learning, Evolution in Computational Intelligence pp 679–689. https://doi.org/10.1007/978-981-15-5788-0_64
3. Akram MU, Khalid S, Khan SA (2013) Identification and classification of microaneurysms for early detection of diabetic retinopathy. Pattern Recogn. 46(1):107–116. https://doi.org/10.1016/j.patcog.2012.07.002
4. Akram MU, Khalid S, Tariq A, Khan SA, Azam F (2014) Detection and classification of retinal lesions for grading of diabetic retinopathy. Comput Biol Med. 45:161–171. https://doi.org/10.1016/j.compbiomed.2013.11.014
5. Alban M, Gilligan T (2016) Automated detection of diabetic retinopathy using fluorescein angiography photographs.
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