This dataset1 contains images of retinal optical coherence tomography, a technique used to capture high-resolution cross sections of retinas that can be used to detect different diseases.
The data can be used to build and train an ML model that can detect ocular diseases.
This repo contains the following structure:
- dataset.csv: CSV file that maps OCT classification labels to the training images.
- data: contains the training images, partitioned into sub directories for the respective ocular diseases. The following are a few example images:
The table below shows a partial example of the data stored in dataset.csv that is used to map classification labels to training images:
| labels | images |
|---|---|
| CNV | data/train/CNV/CNV-451136-177.jpeg |
| DME | data/train/DME/DME-4792882-74.jpeg |
| DRUSEN | data/train/DRUSEN/DRUSEN-7629851-3.jpeg |
| NORMAL | data/train/NORMAL/NORMAL-8088630-3.jpeg |
The labels used in the CSV are:
- CNV: Choroidal Neovascularization
- DME: Diabetic Macular Edema
- DRUSEN: Multiple drusen present in early AMD
- NORMAL: Normal retina
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1 Dataset Credits: https://www.kaggle.com/paultimothymooney/kermany2018