Abstract
Diabetic Macular Edema (DME) is a potentially blinding consequence of Diabetic Retinopathy (DR) as well as the leading cause of vision loss in diabetics. DME is characterized by a buildup of extracellular fluid inside the macula through hyperpermeable vessels. The presence of DME can be spotted at any level of DR with varying degrees of severity using prominent imaging modalities such as Color Fundus Photography (CFP) and Optical Coherence Tomography (OCT). Computerized approaches for screening eye disorders appear to be beneficial, as they provide doctors with detailed insights into abnormalities. Such a system for the evaluation of retinal images can function as a stand-alone disease monitoring system. This review reports the state-of-art automated DME detection methods with traditional Machine Learning (ML) and Deep Learning (DL) techniques employing retinal fundus or OCT images. The paper provides a list of public retinal OCT and fundus imaging datasets for DME detection. In addition, the paper describes the dynamics of advancements in presented methods adopted in the past along with their strengths and limitations to highlight the insufficiencies that could be addressed in future investigations.
Published Version
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