Machine Learning Use Case: Ocular Disease Recognition

Ocular diseases are extensively-studied in the healthcare world as they affect millions of people. With this in mind, we decided to build an ML model in PerceptiLabs that applies image recognition techniques on fundus images to detect possible cataracts in patients. Using a model like this could help doctors, optometrists, and researchers to more easily classify and detect such conditions. To train our model, we grabbed the Ocular Disease Recognition dataset on Kaggle that comprises fundus images representing seven ocular-related conditions and well as normal images (i.e., those depicting no-ocular-related conditions). For our use case, we narrowed down the dataset to 293 images representing normal images and 293 representing cataracts.