Problem Statement :

Chest X-ray exam is one of the most frequent and cost-effective medical imaging examination. However clinical diagnosis of chest X-ray can be challenging, and sometimes believed to be harder than diagnosis via chest CT imaging. To achieve clinically relevant computer-aided detection and diagnosis (CAD) in real world medical sites on all data settings of chest X-rays is still very difficult, if not impossible when only several thousands of images are employed for study.

Started in 1953, National Institute of Health - Clinical Centre is one the leading hospitals in US. They are the active partners in medical discovery. Currently, there are around 1600 clinical research studies in progress at NIH centre, USA. With a support staff of around 620 nurses, in 2016, they handled more than 10,400 new patient

As a part of a research study to explore deep learning techniques, NIH has recently open-sourced their dataset of frontal chest X-ray images of patients.

Your task is to identify the class of thorax diseases from the given chest x-ray images.

Download test and train image set from: https://drive.google.com/drive/folders/13cx4SBFLTX8CqIqjjec9-pcadGaJ0kNj