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- W2711924351 abstract "Diabetic Retinopathy (DR) is one of the leading causes of blindness worldwide. Detecting DR and grading its severity is essential for disease treatment. Convolutional neural networks (CNNs) have achieved state-of-the-art performance in many different visual classification tasks. In this paper, we propose to combine CNNs with dictionary based approaches, which incorporates pathology specific image representation into the learning framework, for improved DR severity classification. Specifically, we construct discriminative and generative pathology histograms and combine them with feature representations extracted from fully connected CNN layers. Our experimental results indicate that the proposed method shows improvement in quadratic kappa score (κ <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> = 0.86) compared to the state-of-the-art CNN based method (κ <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> = 0.81)." @default.
- W2711924351 created "2017-06-30" @default.
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- W2711924351 date "2017-04-01" @default.
- W2711924351 modified "2023-10-01" @default.
- W2711924351 title "A novel hybrid approach for severity assessment of Diabetic Retinopathy in colour fundus images" @default.
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- W2711924351 doi "https://doi.org/10.1109/isbi.2017.7950703" @default.
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