Abstract:
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.