Blindness (Diabetic Retinopathy) Severity Scale Detection
- URL: http://arxiv.org/abs/2110.01333v1
- Date: Mon, 4 Oct 2021 11:31:15 GMT
- Title: Blindness (Diabetic Retinopathy) Severity Scale Detection
- Authors: Ramya Bygari, Rachita Naik, Uday Kumar P
- Abstract summary: Diabetic retinopathy (DR) is a severe complication of diabetes that can cause permanent blindness.
Timely diagnosis and treatment of DR are critical to avoid total loss of vision.
We propose a novel deep learning based method for automatic screening of retinal fundus images.
- Score: 0.0
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: Diabetic retinopathy (DR) is a severe complication of diabetes that can cause
permanent blindness. Timely diagnosis and treatment of DR are critical to avoid
total loss of vision. Manual diagnosis is time consuming and error-prone. In
this paper, we propose a novel deep learning based method for automatic
screening of retinal fundus images to detect and classify DR based on the
severity. The method uses a dual-path configuration of deep neural networks to
achieve the objective. In the first step, a modified UNet++ based retinal
vessel segmentation is used to create a fundus image that emphasises elements
like haemorrhages, cotton wool spots, and exudates that are vital to identify
the DR stages. Subsequently, two convolutional neural networks (CNN)
classifiers take the original image and the newly created fundus image
respectively as inputs and identify the severity of DR on a scale of 0 to 4.
These two scores are then passed through a shallow neural network classifier
(ANN) to predict the final DR stage. The public datasets STARE, DRIVE, CHASE
DB1, and APTOS are used for training and evaluation. Our method achieves an
accuracy of 94.80% and Quadratic Weighted Kappa (QWK) score of 0.9254, and
outperform many state-of-the-art methods.
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