This use case cover a retinal OCT images . Retinal optical coherence tomography (OCT) is an imaging technique used to capture high-resolution cross sections of the retinas. Arount 30M of scans are done each year, and it takes amount of time to analyse them. In this use case we are going to classify this types of images in four classes: choroidal neovascularization (CNV), Diabetic macular edema (DME), Multiple drusen present in early AMD (Drusen) and normal retina.
We based on the Retinal OCT dataset from kaggle. This dataset is unbalanced as we can see in the next image:
We choose only 4k images of each class to balance the dataset for our use case.
The dataset is accesible using Perceptilabs github
|Merge||3 Inputs with same link of input source|