Abstract:
"
Radiography is a prevalent method of medical diagnosis, especially in humans. Its
popularity and increased usage are due to the affordability and the convenience of its
procedures. This is used for a wide variety of cases in medical environments. Out of those,
Chest Radiography or Chest X-Rays holds a significant place due to the numerous diseases
that could be diagnosed by Chest X-Rays. These diseases vary from low-risk diseases to
high-risk, life-threatening diseases. Due to this, accurate diagnosis of Chest X-Rays is
considered very crucial. However, human errors are inevitable. In some parts of the world,
medical professionals with extensive experience in Chest X-Ray diagnosis are scarce in
numbers. Machine Learning attempts to provide a solution for these two issues of
misdiagnosis and lack of medical professionals. Existing attempts are mostly based on
Deep Convolutional Neural Networks. This dissertation presents a novel way of utilizing
multiple neural networks for the purpose of accurate detection and localization of diseases
present in Chest X-Ray images. The proposed algorithm creates a range of new pathways
to conduct research in a variety of fields and use cases. However, this dissertation
primarily aims to prove the strengths and advantages of the proposing algorithm for Chest
X-Ray classification within a well-defined scope. The dissertation further presents the
limitations, and its drawbacks backed up with extensive testing and evaluation procedures
and techniques employing the experts.
"
