Multi-retinal disease classification by reduced deep learning features

100 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks (2019, background): “Background of the disease: As people with diabetes have a high prevalence for RDR [47], a frequent retinal screening is recommended and deep learning algorithms have been successfully developed to classify fundus images ([8], [20], [3], [46]).”
Deep Learning and Its Applications in Biomedicine (2018, methodology): “DBN Segmentation of left ventricle of the heart from MR data [112] Discriminate retinal-based diseases [139]”
Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images (2017, background): “While ﬁrst approaches using neural networks to detect diabetic retinopathy on retinal images without additional feature extraction showed a low classiﬁcation accuracy [5, 6], recent approaches based on deep neural networks [7, 8, 9] report good performance.”