Identification of pneumonia on chest x-ray images through machine
  learning
Eduardo Augusto Roeder
Pneumonia is the leading infectious cause of infant death in the world. When
identified early, it is possible to alter the prognosis of the patient, one
could use imaging exams to help in the diagnostic confirmation. Performing and
interpreting the exams as soon as possible is vital for a good treatment, with
the most common exam for this pathology being chest X-ray. The objective of
this study was to develop a software that identify the presence or absence of
pneumonia in chest radiographs. The software was developed as a computational
model based on machine learning using transfer learning technique. For the
training process, images were collected from a database available online with
children's chest X-rays images taken at a hospital in China. After training,
the model was then exposed to new images, achieving relevant results on
identifying such pathology, reaching 98% sensitivity and 97.3% specificity for
the sample used for testing. It can be concluded that it is possible to develop
a software that identifies pneumonia in chest X-ray images.