Timely diagnosis of diseases has been at the core of artificial intelligence in healthcare for the last 50 years. To accurately diagnose ailments, doctors need every possible tool at their disposal. Now with the growing use of machine learning (ML) in healthcare, we wanted to see how quickly we could create an image classification model that could give doctors an upper hand in diagnosing patients’ ailments, specifically pneumonia.
To accomplish this, we built an ML model that can classify images of chest x-rays as either normal or infected. This involved included preparing and wrangling the training data, building a .csv file to map that data to the two classifications, and iterating with the model in PerceptiLabs. Let’s see how we did!