Proceedings ArticleDOI
Diabetic Retinopathy Classification using a Combination of EfficientNets
Sagar Karki,Pradnya Kulkarni +1 more
- pp 68-72
TLDR
In this article, the authors proposed a method for classifying the severity of diabetic retinopathy using deep learning and achieved a quadratic kappa score of 0.924377 on the APTOS test dataset.Abstract:
Diabetic Retinopathy (DR) is a diabetes complication that affects vision. It is caused by damage to the blood vessels of retina. Early and accurate detection of DR is crucial to reduce likelihood of progression to proliferative retinopathy and blindness. This paper proposes a method for classifying the severity of DR using deep learning. Experiments were conducted by blending the members of EfficientNet for classification of the diabetic retinopathy image as no DR, mild, moderate, severe, or proliferative DR. The models have been trained using different datasets and best model achieved a quadratic kappa score of 0.924377 on the APTOS test dataset. The results are promising and warrant further investigation. The presented model has the potential aid in fast diagnosis for better early detection of DR.read more
Citations
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Journal ArticleDOI
Diabetic Retinopathy Detection From Fundus Images Using Multi-Tasking Model With EfficientNet B5
TL;DR: This work has developed a model to identify the different stages of Diabetic Retinopathy using Deep learning and has achieved 87% of accuracy with the help of EfficientNet model.
Journal ArticleDOI
A Survey on Deep-Learning-Based Diabetic Retinopathy Classification
TL;DR: In this paper , the authors surveyed the existing literature on diabetic retinopathy diagnoses from fundus images using deep learning and provided a brief description of the current DL techniques that are used by researchers in this field.
Proceedings ArticleDOI
Diabetic Retinopathy Detection using MobileNetV2 Architecture
TL;DR: In this article , two CNN models, one binary classification and another multinomial classification model, were proposed to detect diabetic retinopathy and further classify diabetic retinal disease into five distinct and widely used stages -none, mild, moderate, severe and Proliferative.
Proceedings ArticleDOI
Classification of Diabetic Retinopathy Fundus Images using Deep Neural Network
TL;DR: The Fundus photography of the rear of an eye is used by Machine Learning process that identifies the disease by comparing eye fundus images of patients at an angle of 45 degrees centered on fovea and optic disc to finalize the type of disease or abnormality.
Book ChapterDOI
Diabetic Retinopathy Detection Using Deep Learning Models
TL;DR: In this paper , a comparative analysis is done with various deep learning models like CNN, MobileNetv2, ResNet50, Inceptionv2 and DenseNet and the best model is proposed which is used to make predictions and attain accuracy using lesser number of images.
References
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Posted Content
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Proceedings Article
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Mingxing Tan,Quoc V. Le +1 more
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