DR-CNN+ Approach for Standardized Diabetic Retinopathy Severity Assessment
Abstract
Diabetic retinopathy (DR) is a serious eye disorder that damages the retina and can lead to vision impairment and blindness, especially in individuals with diabetes. Early identification is crucial for a positive outcome, however, diabetic retinopathy can only be diagnosed with color fundus photographs, which is a technique that is difficult and time-consuming. To address this issue, this paper presents a Deep Learning-based algorithm that utilizes DR -convolutional neural network+ (DR-CNN+) to classify retinal pictures into different stages of diabetic retinopathy. The proposed algorithm is trained on a dataset of 11000 colored retinal pictures from the training set and 2200 photos from the testing set. The simulation results demonstrate that the DR-CNN+-based algorithm can achieve high levels of accuracy, sensitivity, and specificity. Overall, this paper highlights the potential of using deep learning and CNNs to improve the detection and grading of diabetic retinopathy, which could have a significant impact on the prevention of blindness caused by this disease.
Keywords
References
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Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
ISSN 2089-3272
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