  The potential of deep learning, especially in medical imaging, initiated
astonishing results and improved the methodologies after every passing day.
Deep learning in radiology provides the opportunity to classify, detect and
segment different diseases automatically. In the proposed study, we worked on a
non-trivial aspect of medical imaging where we classified and localized the
X-Rays affected by bullets. We tested Images on different classification and
localization models to get considerable accuracy. The replicated data set used
in the study was replicated on different images of chest X-Rays. The proposed
model worked not only on chest radiographs but other body organs X-rays like
leg, abdomen, head, even the training dataset based on chest radiographs.
Custom models have been used for classification and localization purposes after
tuning parameters. Finally, the results of our findings manifested using
different frameworks. This might assist the research enlightening towards this
field. To the best of our knowledge, this is the first study on the detection
and classification of radiographs affected by bullets using deep learning.
