Enhancing the accuracy and speed of the detection process can potentially have a significant impact on population health via early diagnosis and intervention.

In this paper, we propose a novel and automatic diabetic retinopathy (DR) detection method using deep convolutional neural networks (DCNNs). To identify the ...

This paper, review the most recent studies on the detection of DR by using one of the efficient algorithms of deep learning, which is Convolutional Neural ...

In this paper, we propose three novel automatic DR detection methods based on deep convolutional neural networks. The results illustrate that our methods ...

Convolutional neural networks with synaptic metaplasticity are suitable for early detection of diabetic retinopathy due to their fast convergence rate, ...

The importance of an automatic method for diabetic retinopathy detection has been recognized. In our study, we focus on the classification of retinal images ...

This paper presents the design and implementation of GPU accelerated deep convolutional neural networks to automatically diagnose and thereby classify high- ...

Introduced a model to automate the process to detect the severity of diabetic retinopathy using Convolutional Neural Network and Residual Blocks (DRCNNRB).

Nov 29, 2022 · This paper proposes a comprehensive model using 26 state-of-the-art DL networks to assess and evaluate their performance, and which contribute for deep feature ...

Jan 20, 2023 · The model achieves 93.72% accuracy, 97.30% sensitivity, and 92.90% specificity on the test data as labelled by clinical experts on Diabetic Retinopathy.