{"url":"/dataset/labeled-retinal-optical-coherence-tomography","name":"Labeled Retinal Optical Coherence Tomography Dataset for Classification of Normal, Drusen, and CNV Cases","full_name":null,"description_markdown":"This dataset consists of more than 16,000 retinal OCT B-scans from 441 cases (Normal: 120, Drusen: 160, CNV: 161) and is acquired at Noor Eye Hospital, Tehran, Iran. Images are labeled by a retinal specialist.\r\n\r\nThe structure of the folders are as below:\r\n- CNV, DRUSEN, NORMAL folders\r\n- Within each class, folders are separated patient-wise with numbers from 1 to <number_of_patients>.\r\n- Within each patient folder, images (B-scans) are labeled with <0XX_LABEL> format where <XX> is the B-scan number, and <LABEL> is the specialist's selected label for that specific B-scan.\r\n\r\nThe excel spreadsheet (data_information.csv) includes information such as \"Patient ID\", \"Class\", \"Eye\", \"B-scan\", \"Label\", and \"Directory\" for all images (16823 rows, 6 columns).\r\n\r\nThe python code (read_data.py) includes code for loading images and labels as NumPy arrays. The written function outputs the input data as an array with shape (number_of_images, imageSize, imageSize, 3) and output data as a list of labels (Normal: 0, Drusen: 1, CNV: 2). There are two different options for reading the files:\r\n- Option 1: Reading all images. This would result in 16822 images.\r\n- Option 2: Reading the worst-case condition images for each volume (i.e., if a patient was detected as a CNV case, only CNV-appearing B-scans were included for training procedure and normal and drusen B-scans of that patient are excluded from the dataset). This would result in 12649 images.","description_withheld":null,"homepage":"https://data.mendeley.com/datasets/8kt969dhx6/1","introduced_date":"2021-10-06","introduced_date_note":null,"introduced_by":{"paper":"/paper/multi-scale-convolutional-neural-network-for-1","title":"Multi-Scale Convolutional Neural Network for Automated AMD Classification using Retinal OCT Images","first_author":"Saman Sotoudeh-Paima","url":null},"license":{"name":"CC BY 4.0","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Medical","url":"/datasets/modality/medical"}],"tasks":[],"languages":[],"variants":["Labeled Retinal Optical Coherence Tomography Dataset for Classification of Normal, Drusen, and CNV Cases"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}