Labeled Retinal Optical Coherence Tomography Dataset for Classification of Normal, Drusen, and CNV Cases

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.

The structure of the folders are as below:
- CNV, DRUSEN, NORMAL folders
- Within each class, folders are separated patient-wise with numbers from 1 to .
- Within each patient folder, images (B-scans) are labeled with <0XX_LABEL> format where is the B-scan number, and is the specialist's selected label for that specific B-scan.

The 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).

The 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:
- Option 1: Reading all images. This would result in 16822 images.
- 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.

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If you utilize the dataset, kindly acknowledge and cite our work by referencing the following publication:
Sotoudeh-Paima, S., Jodeiri, A., Hajizadeh, F., & Soltanian-Zadeh, H. (2022). Multi-scale convolutional neural network for automated AMD classification using retinal OCT images. Computers in biology and medicine, 144, 105368.

The following repository contains implementation of the abovementioned publication:
https://github.com/SamanSotoudeh/Multi-scale-convolutional-neural-network-for-automated-AMD-classification-using-retinal-OCT-images