Image Features for Tuberculosis Classification in Digital Chest Radiographs

<p>Tuberculosis (TB) is a respiratory disease which affects millions of people each year, accounting for the tenth leading cause of death worldwide, and is especially prevalent in underdeveloped regions where access to adequate medical care may be limited. Analysis of digital chest radiographs (CXRs) is a common and inexpensive method for the diagnosis of TB; however, a trained radiologist is required to interpret the results, and is subject to human error. Computer-Aided Detection (CAD) systems are a promising machine-learning based solution to automate the diagnosis of TB from CXR images. As the dimensionality of a high-resolution CXR image is very large, image features are used to describe the CXR image in a lower dimension while preserving the elements in the CXR necessary for the detection of TB. In this thesis, I present a set of image features using Pyramid Histogram of Oriented Gradients, Local Binary Patterns, and Principal Component Analysis which provides high classifier performance on two publicly available CXR datasets, and compare my results to current state-of-the-art research.</p>

Name thesis:degree_name

Master of Science (MS)

Discipline thesis:degree_discipline

Computational Science

Year dc:date.available

2020

Author dc:creator

• Hooper, Brian

Contributors dc:contributor

• Szilard Vajda
• Donald Davendra
• Razvan Andonie

• Automatic chest x-ray analysis
• Feature selection
• Neural networks
• PHOG
• Machine Learning
• Automatic TB screening
• Artificial Intelligence and Robotics
• Data Science

Repository record dc:identifier

https://digitalcommons.cwu.edu/etd/1356

OAI identifier oai:identifier

oai:digitalcommons.cwu.edu:etd-2380