Convolutional Neural Networks for Diabetic Retinopathy Detection in Retinal Images
Keywords:
Diabetic retinopathy, Ocular ailment, Uncontrolled diabetes, Elevated blood, sugar, Convolutional Neural Networks (CNN), Deep learning models, Medical imagingAbstract
Diabetic retinopathy is a degenerative eye disease associated with uncontrolled diabetes, high blood pressure, blood sugar, cholesterol, and body weight. If diabetes-related diabetic retinopathy is not detected and treated in its early stages, it might result in visual impairment. Traditional methods of diagnosis often require manual examination by trained ophthalmologists, which can be time-consuming and subjective. Deep learning models called Convolutional Neural Networks (CNNs) are renowned for their capacity to recognize small patterns and features in images, which makes them very useful for challenging medical imaging applications. In this study, we proposed two CNN architectures, namely CNN-Plain and CNN-BN-D to automate and enhance the diagnostic process for diabetic retinopathy (DR) detection. The model's effectiveness is assessed using a range of criteria, such as accuracy, precision, recall, and F1Score, to confirm that it is as effective as current diagnostic techniques. The results demonstrate the CNN-BN-D model with 0.94 accuracy on train and test data exhibits superior performance and generalization compared to the simpler CNN-Plain architecture with 0.89 accuracy in the task of diabetic retinopathy detection.
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