ArXiv Preprint
Biomedical image segmentation is one of the fastest growing fields which has
seen extensive automation through the use of Artificial Intelligence. This has
enabled widespread adoption of accurate techniques to expedite the screening
and diagnostic processes which would otherwise take several days to finalize.
In this paper, we present an end-to-end pipeline to segment lungs from chest
X-ray images, training the neural network model on the Japanese Society of
Radiological Technology (JSRT) dataset, using UNet to enable faster processing
of initial screening for various lung disorders. The pipeline developed can be
readily used by medical centers with just the provision of X-Ray images as
input. The model will perform the preprocessing, and provide a segmented image
as the final output. It is expected that this will drastically reduce the
manual effort involved and lead to greater accessibility in
resource-constrained locations.
Shashank Shekhar, Ritika Nandi, H Srikanth Kamath
2022-12-09
