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ResNet18 for Diabetic Retinopathy Detection by farizky wahyudi is a document available to read on EtoBox.

What is ResNet18 for Diabetic Retinopathy Detection about?

This paper presents a refined ResNet18 architecture utilizing the Swish activation function for the classification of Diabetic Retinopathy (DR) from fundus images. The proposed model achieved an accuracy of 93.51%, along with high sensitivity and precision, outperforming other deep learning architectures in DR detection. The study highlights the importance of automated screening for early DR detection and addresses challenges in retinal image classification using deep learning techniques.

Author

farizky wahyudi

Language

EN