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Dual Branch Deep Learning Network for Detection and Stage Grading of Diabetic Retinopathy by Hossein Shakibania; Sina Raoufi; Behnam Pourafkham; Hassan Khotanlou; Muharram Mansoorizadeh is a Medicine article available to read on EtoBox.

What is Dual Branch Deep Learning Network for Detection and Stage Grading of Diabetic Retinopathy about?

Diabetic retinopathy is a severe complication of diabetes that can lead to permanent blindness if not treated promptly. Early and accurate diagnosis of the disease is essential for successful treatment. This paper introduces a deep learning method for the detection and stage grading of diabetic retinopathy, using a single fundus retinal image. Our model utilizes transfer learning, employing two state-of-the-art pre-trained models as feature extractors and fine-tuning them on a new dataset. The proposed model is trained on a large multi-center dataset, including the APTOS 2019 dataset, obtained from publicly available sources. It achieves remarkable performance in diabetic retinopathy detection and stage classification on the APTOS 2019, outperforming the established literature. For binary classification, the proposed approach achieves an accuracy of 98.50%, a sensitivity of 99.46%, and a specificity of 97.51%. In stage grading, it achieves a quadratic weighted kappa of 93.00%, an accuracy of 89.60%, a sensitivity of 89.60%, and a specificity of 97.72%. The proposed approach serves as a reliable screening and stage grading tool for diabetic retinopathy, offering significant potentia

Who reads Dual Branch Deep Learning Network for Detection and Stage Grading of Diabetic Retinopathy?

Author

Hossein Shakibania; Sina Raoufi; Behnam Pourafkham; Hassan Khotanlou; Muharram Mansoorizadeh

Publisher

Elsevier BV

Published

2024

Language

EN

Field

Medicine (Physical Sciences)