The possibility of the combination of OCT and fundus images for improving the diagnostic accuracy of deep learning for age-related macular degeneration: a preliminary experiment
Version 1This dataset was designed to support research in AI-based medical image analysis, particularly focusing on retinal and pulmonary conditions. It includes thousands of expertly labeled OCT and Chest X-Ray images sourced from independent patients and categorized into four classes: CNV, DME, DRUSEN, and NORMAL. The dataset mirrors the imaging data described in the publication 'Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning', and is structured to facilitate reproducibility and benchmarking in deep learning workflows.
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This dataset is an upgraded version of the dataset used in the latter part of the article 'The possibility of the combination of OCT and fundus images for improving the diagnostic accuracy of deep learning for age-related macular degeneration: a preliminary experiment' in Medical & Biological Engineering & Computing volume 57, pages677-687(2019).
Images including both OCT and matched fundus photograph was crawled from Google Image Search using keywords related to normal retina, drusen, AMD, and OCT. The dataset was built by harvesting images manually for normal and AMD categories. Finally, 59 normal eyes, 26 drusens, and 98 AMD eyes including both OCT and matched fundus images were obtained.