… Feature fusion through dense deep Feature fusion applying through deep features extracted
… The 2 max pooling and average cross pooling can be performed for dense deep valuation. …

… The proposed approach uses deep features of ResNet-50 along with Random Forest as a
classifier for the detection and grading of diabetic retinopathy. High-level features obtained …

… Learning (ML) algorithms in detecting DR in the colored fundus. In … visible diabetic retinopathy
and can be detected early with … and deep learning applications can quickly detect DR in …

… detect and diagnose Diabetic Retinopathy (DR) from the eye fundus images using hybrid
deep learning features. The … detect and diagnose DR from eye fundus images using hybrid …

… deep features learned from DCNN for detecting HEs. It achieves a new state-of-the-art in both
detecting … Furthermore, the proposed feature selection and fusion strategy reduces feature …

… 2015 in image classification, detection, and localization. The concept of residual learning as
a shortcut connection can accelerate the convergence of deep networks, thus giving ResNet …

… to classifying diabetic retinopathy using deep convolutional … scaling, feature extraction,
feature fusion and feature selection. … grading for the detection of referable diabetic retinopathy,” …

… retinopathy detection and classification that used deep learning techniques. The common
fundus DR datasets that are publicly available have been described, and deep… the detection of …

… the analysis of retinal images to detect the three major signs of diabetic retinopathy: exudates,
… detection of diabetic retinopathy without requiring a large dataset for training the network. …

One of the major causes of blindness in human beings is the diabetic retinopathy (DR). To
prevent blindness, early detection of DR is therefore necessary. In this paper, a hybrid model …