Abstract
INTRODUCTION: Diabetic eye disease (DED) is a group of eye problems that can affect diabetic people. Such disorders include diabetic retinopathy, diabetic macular edema, cataracts, and glaucoma. Diabetes can damage your eyes over time, which can lead to poor vision or even permanent blindness. Early
Highlights
Diabetic eye disease (DED) is a group of eye problems that can affect diabetic people
We present conceptual system architecture with pre-trained Convolutional Neural Network combined with image processing techniques to construct an early DED detection system
This study was intended to propose the framework for early automatic DED detection of fundus images through deep learning addressing three main research gaps
Summary
Diabetic eye disease (DED) is a group of eye problems that can affect diabetic people. Such disorders include diabetic retinopathy, diabetic macular edema, cataracts, and glaucoma. Research difficulties in early detection of DEDs can so far be summarized as follows: changes in the eye anatomy during its early stage are often untraceable by the human eye due to the subtle nature of the features, where large volumes of fundus images put tremendous pressure on scarce specialist resources, making manual analysis practically impossible. Manual extraction of features from a large volume of fundus images and the discovery of beneficial learning information from these images results in a loss of time between detection and treatment.
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