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

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. ***************************************************************************************************************************************************** ***************************************************************************************************************************************************** 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: github.com/SamanSotoudeh/Multi-scale-convolutional-neural-network-for-automated-AMD-classification-using-retinal-OCT-images