  As advancements in technology and medicine are being made, many countries are
still unable to access quality medical care due to cost and lack of qualified
medical personnel. This discrepancy in healthcare has caused many preventable
deaths, either due to lack of detection or lack of care. One of the most
prevalent diseases in the world is pneumonia, an infection of the lungs that
killed 2.56 million people worldwide in 2017. In this same year, the United
States recorded a pneumonia death rate of 15.88 people per 100000 in
population, while much of Sub-Saharan Africa, such as Chad and Guinea,
experienced death rates of over 150 people per 100000. In sub-Saharan Africa,
there is an extreme shortage of doctors and nurses, estimated to be around 2.4
million. The hypothesis being tested is that a deep learning model can receive
input in the form of an x-ray and produce a diagnosis with the equivalent
accuracy of a physician, compared to a prediagnosed image. The model used in
this project is a modified convolutional neural network. The model was trained
on a set of 2000 x-ray images that have predetermined normal and abnormal lung
findings, and then tested on a set of 400 images that contains evenly split
images of pneumonia and healthy lungs. For each computer-run test, data was
collected on a base measurement of accuracy, as well as more specific metrics
such as specificity and sensitivity. Results show that the algorithm tested was
able to accurately identify abnormal lung findings an average of 82.5% of the
time. The model achieved a maximum specificity of 98.5% and a maximum
sensitivity of 90% separately, and the highest simultaneous values of these two
metrics was a sensitivity of 90% and a specificity of 78.5%. This research can
be further improved by testing other deep learning models as well as machine
learning models to improve the metric scores and chance of correct diagnoses.
