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Download PDFOpen PDF in browserA Deep Learning Based Approach for Classification of Diabetic RetinopathyEasyChair Preprint 111967 pages•Date: October 29, 2023AbstractDiabetes Mellitus (DM) is a metabolic disorder happens because of high blood sugar level in the body. Over the time, diabetes creates eye deficiency also called as Diabetic Retinopathy (DR) causes major loss of vision. In recent times computer vision with Deep Neural Networks can train a model perfectly and level of accuracy also will be higher than other neural network models. In this study fundus images containing diabetic retinopathy has been taken into consideration. This paper proposes an automated knowledge model to identify the key antecedents of DR. We have tested our network on the largest publicly available Kaggle diabetic retinopathy dataset, and achieved 0.851 quadratic weighted kappa score and 0.844 AUC score, which achieves the state-of-the-art performance on severity grading. In the earlystage detection, we have achieved a sensitivity of 98% and specificity of above 94%, which demonstrates the effectiveness of our proposed method. Our proposed architecture is at the same time very simple and efficient with respect to computational time and space are concerned. The Deep Learning models are capable of quantifying the features as blood vessels, fluid drip, exudates, hemorrhages and micro aneurysms into different classes. The foremost challenge of this study is the accurate verdict of each feature class thresholds. The model will be helpful to identify the proper class of severity of diabetic retinopathy images. Keyphrases: Computer Aided Diagnosis, Convolutional Neural Network, Deep Neural Network, Diabetic Retinopathy Download PDFOpen PDF in browser |
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