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 DRCNN+-based algorithm can achieve high levels of accuracy, sensitivity, and specificity. Our proposed DR-CNN+ model not only improves diagnostic performance for diabetic retinopathy severity evaluation, but it also saves training time by 95% when compared to current models." 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.
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References
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Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
ISSN 2089-3272
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