A Computational-Intelligence-Based Approach for Detection of Exudates in Diabetic Retinopathy Images

By A. Osareh, Bita Shadgar, Richard Markham. IEEE Transactions on Information Technology in Biomedicine, 2009

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Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images (2017, background): “A lot of research has been conducted to detect speciﬁc lesion areas, like blood vessel transformations, exudates, microaneurysms and hemorrhages [10, 11, 12, 13, 14].”
A hybrid intelligent system for medical data classification (2014, methodology): “Osareh, Shadgar, and Markham (2009) used an MLP neural network with a genetic-based algorithm for”
IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge. (2020, methodology): “Similarly, for EXs, researchers relied on approaches like clustering ( Osareh et al., 2009 ), model-based ( Sánchez et al., 2009; Harangi and Hajdu, 2014 ), ant colony optimization (ACO) ( Pereira et al., 2015 ) and contextual information ( Sánchez et al., 2012 ).”