Abstract:
Machine Learning, a subtype of AI, enables computers to mimic human behavior without explicit programming. This study aims to apply automated machine learning to the diagnosis of medical images to make machine learning more accessible to clinicians and doctors. In this study, a dataset containing 2313 images each of covid-19, pneumonia and normal chest x-rays was selected and divided into testing, training, and validation datasets and the AutoGluon library was used to train and produce a model that would classify an input image and infer the probable diagnosis from the diseases it was trained upon. This study proves that applying AutoML techniques such as hyperparameter optimization and neural architecture search can produce models with high accuracy for medical image diagnosis.
