AI-supported system for the analysis of retinal images for the automated detection of diabetic retinopathy

Abstract

An AI-based retinal image analysis system for the automated detection of diabetic retinopathy, comprising the following: an image acquisition unit consisting of an optical arrangement with a coaxial illumination source, a multi-lens arrangement and an image sensor for capturing retinal images; a preprocessing unit that is operationally coupled with the image acquisition unit and is configured to receive the retinal fundus images and perform image normalization including illumination correction, noise reduction and contrast enhancement; a feature extraction unit comprising at least one processor configured to perform a variety of folding operations on the preprocessed retinal fundus images to generate hierarchical feature representations corresponding to the anatomical structures and pathological regions of the retina; a classification unit comprising at least one processor configured to process the hierarchical feature representations and produce a severity classification output corresponding to the stages of diabetic retinopathy; a storage unit that is operationally coupled to the feature extraction unit and the classification unit and stores the trained parameters assigned to the feature extraction unit and the classification unit; and a display unit configured to show the user the severity classification output and the corresponding diagnostic information.

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