  In this work, we present an end-to-end deep learning framework for X-ray
image diagnosis. As the first step, our system determines whether a submitted
image is an X-ray or not. After it classifies the type of the X-ray, it runs
the dedicated abnormality classification network. In this work, we only focus
on the chest X-rays for abnormality classification. However, the system can be
extended to other X-ray types easily. Our deep learning classifiers are based
on DenseNet-121 architecture. The test set accuracy obtained for 'X-ray or
Not', 'X-ray Type Classification', and 'Chest Abnormality Classification' tasks
are 0.987, 0.976, and 0.947, respectively, resulting into an end-to-end
accuracy of 0.91. For achieving better results than the state-of-the-art in the
'Chest Abnormality Classification', we utilize the new RAdam optimizer. We also
use Gradient-weighted Class Activation Mapping for visual explanation of the
results. Our results show the feasibility of a generalized online projectional
radiography diagnosis system.
