18 datasets found

SUSTech-SYSU dataset is a collection of images of diabetic retinopathy.

The dataset used for the proposed Concept-Centric Visual Turing Test (VTT) framework for evaluating MIC methods.

MAPLES-DR is a dataset containing new diagnoses for Diabetic Retinopathy (DR) and Macular Edema (ME) as well as new pixel-wise segmentation maps for 10 anatomical and...

A public diabetic retinopathy dataset containing 5,590 images with image-level grading labels.

A public diabetic retinopathy dataset containing 88,702 images with image-level grading labels.

The APTOS 2019 blindness detection competition dataset is used for training and testing the proposed Frequency Domain Convolutional Neural Network (FDCNN) architecture.

The IDRiD dataset consists of 516 fundus images and ground truths associated with the signs of Diabetic Retinopathy (DR) and Diabetic Macular Edema (DME).

APTOS2019 is a dataset of 3,662 fundus images for diabetic retinopathy classification.

Retinal fundus images have been extensively used for clinical analysis of various ocular diseases, but the quality of fundus images is critical to the diagnosis and screening of...

The dataset used in the diabetic retinopathy detection task in the conformal risk control framework.

The APTOS-2019 blindness detection dataset is used for detecting diabetic retinopathy severity stages from fundus images.

The Kaggle-DR dataset is used for diabetic retinopathy grading, containing 35,126 high-resolution fundus images.

A dataset of retinal images for the classification of diabetic retinopathy and age-related macular degeneration.

The EyePACS dataset consists of 35,126 training images and 53,576 testing images only containing grading labels.

The Isfahan MISP dataset, consisting of retinography and angiography images of diabetic patients.

The dataset used in this paper is not explicitly described. However, it is mentioned that the authors used a dataset for medical image classification, specifically for diabetic...

A dataset of retinal images for the detection of diabetic retinopathy and age-related macular degeneration.

A video dataset consisting of 31 video clips captured by cameras in different scenarios, with a total duration of 74 minutes.