  In this era of pandemic, the future of healthcare industry has never been
more exciting. Artificial intelligence and machine learning (AI & ML) present
opportunities to develop solutions that cater for very specific needs within
the industry. Deep learning in healthcare had become incredibly powerful for
supporting clinics and in transforming patient care in general. Deep learning
is increasingly being applied for the detection of clinically important
features in the images beyond what can be perceived by the naked human eye.
Chest X-ray images are one of the most common clinical method for diagnosing a
number of diseases such as pneumonia, lung cancer and many other abnormalities
like lesions and fractures. Proper diagnosis of a disease from X-ray images is
often challenging task for even expert radiologists and there is a growing need
for computerized support systems due to the large amount of information encoded
in X-Ray images. The goal of this paper is to develop a lightweight solution to
detect 14 different chest conditions from an X ray image. Given an X-ray image
as input, our classifier outputs a label vector indicating which of 14 disease
classes does the image fall into. Along with the image features, we are also
going to use non-image features available in the data such as X-ray view type,
age, gender etc. The original study conducted Stanford ML Group is our base
line. Original study focuses on predicting 5 diseases. Our aim is to improve
upon previous work, expand prediction to 14 diseases and provide insight for
future chest radiography research.
