DREAM: Diabetic Retinopathy Analysis Using Machine Learning

By Sohini Roychowdhury, Dara D. Koozekanani, Keshab K. Parhi. IEEE journal of biomedical and health informatics, 2014

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Machine Learning and Data Mining Methods in Diabetes Research (2017, methodology): “[104] targeted the degree of severity in DR, using a computer-aided screening system (DREAM) that analyzes fundus images with varying illumination and fields of view via machine learning approaches.”
Blood Vessel Segmentation of Fundus Images by Major Vessel Extraction and Subimage Classification (2015, background): “By varying F = [2, 3, 4, 5, 6, 7, 8, 9, 10], we observe the variation in the number of uniquely identified features is F1 = [9, 10, 11, 16, 20, 23, 27, 31, 33].”
IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge. (2020, methodology): “…were devised for multiple lesion detection such as multiscale amplitude-modulation-frequency-modulation ( Agurto et al., 2010 ), machine learning ( Roychowdhury et al., 2014 ), a combination of Hessian multiscale analysis, variational segmentation and texture features ( Figueiredo et al., 2015 ).”
CANet: Cross-Disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading (2019, methodology): “Then the grading was conducted using these extracted features by different machine learning methods [14–21], e.”