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.

This project is part of the Algorithms for Massive Data course organized by the University of Milan, that I recently had the chance to attend. The task is to develop the Deep Learning model able to recognize eye diseases, from eye-fundus images using the TensorFlow library. An important requirement is to make the training process scalable, so create a data pipeline able to handle massive amounts of data points. In this article, I summarize my findings on convolutional neural networks and methods of building efficient data pipelines using the Tensorflow dataset object. Early ocular disease detection is an economic and effective way to prevent blindness caused by diabetes, glaucoma, cataract, age-related macular degeneration (AMD), and many other diseases.