![]() ![]() * The diseases having more than 10 images belong to an independent class and all other disease categories are merged and labeled as “OTHER”. Groundtruth Labels for 28* different categories (Divided into training, validation, and testing set - CSV Files) RFMiD_Challenge_Dataset: It consists ofĢ. Groundtruth Labels for normal and abnormal (comprising of 45 different types of diseases/pathologies) categories (Divided into training, validation, and testing set - CSV Files)ī. Original color fundus images (3200 images divided into a training set (1920 images), validation (640 images), and testing set (640 images) - PNG Files)Ģ. RFMiD_All_Classes_Dataset: It consists ofġ. This dataset will enable the development of generalizable models for retinal screening.Ī. To the best of our knowledge, our dataset, RFMiD, is the only publicly available dataset that constitutes such a wide variety of diseases that appear in routine clinical settings. It consists of 3200 fundus images captured using three different fundus cameras with 46 conditions annotated through adjudicated consensus of two senior retinal experts. To enable development of methods for automatic ocular disease classification of frequent diseases along with the rare pathologies, we have created a new Retinal Fundus Multi-disease Image Dataset (RFMiD). In the past two decades, many publicly available datasets of color fundus images have been collected with a primary focus on diabetic retinopathy, glaucoma, age-related macular degeneration and few other frequent pathologies. One challenge that limits the adoption of computer-aided diagnosis tool by ophthalmologists is the number of sight-threatening rare pathologies, such as central retinal artery occlusion or anterior ischemic optic neuropathy, and others are usually ignored. ![]() Early detection and diagnosis of ocular pathologies would enable forestall of visual impairment. The world faces difficulties in terms of eye care, including treatment, quality of prevention, vision rehabilitation services, and scarcity of trained eye care experts. ![]()
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