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By: Purushotam Naidu K., G. Sri Sivaanandini, P. Divya, Ch. AnuSri, T. Durga Prasanna, and R. Naga Supriya
Diabetic Retinopathy (DR) is an eye condition that arises from diabetes-related complications, making early detection essential for effective treatment. As diabetes advances, an individual’s vision may deteriorate, potentially resulting in diabetic retinopathy. This condition is divided into two primary types: Non-Proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR). Early treatment to maintain a strategic distance from a change less visual impedance is basic as it may save diabetic retinopathy vision. With moves in computer science strategies, openings for the disclosure of DR at the early stages have extended. The chances of recovery will be increased by employing the techniques. In our proposed model, we employed the VGG16 along with the transfer learning techniques for extracting the features from the fundus images. Deep learning techniques, like Convolutional Neural Networks (CNN), DenseNet and machine learning techniques like multivariate decision tree and multiclass SVM ensemble classifier, can be used for classification. The results performed can be evaluated using f1 score, precision, recall, and accuracy.
Keywords: Diabetic Retinopathy, permanent blindness, Deep Learning, Convolutional Neural Networks
Citation:
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