11/23/2021
By Terence Griffin

Title: Deep Learning Applied to Tuberculosis Screening

PhD. Candidate: Terence Griffin
Time: Wednesday, Dec. 1, 2021, 1 p.m.
Location: This will be a virtual defense via Zoom

Committee Members:

  • Yu Cao (advisor), Professor, Department of Computer Science
  • Benyuan Liu (advisor), Professor, Department of Computer Science
  • Yan Luo (member), Professor, Department of Electrical and Computer Engineering

Abstract:
Tuberculosis (TB) is a contagious disease affecting millions of people annually worldwide. TB primarily affects the lungs and is spread through the air when people cough, sneeze, or spit. Providing healthcare professionals with better information, at a faster pace, is essential for combating this disease, especially in Low and Middle Income Countries (LMICs) with resource-constrained health systems.

In this work we investigate the use of deep learning approaches to the problem of TB screening. We first describe how convolution neural networks (CNNs) trained using an object level annotated dataset of chest X-rays (CXRs) allows us to identify the location of pulmonary issues indicative of TB. We compare the performance of Faster R-CNN, Mask R-CNN, Cascade versions of each, and SOLOv2, demonstrating reasonable results with well annotated dataset. We present a method to reduce the false positive rate by comparing the location of a detected object with the known location of areas where the detected class is likely to occur in the lung. Our results show that object detection and instance segmentation of CXRs can be achieved with a dataset of high-quality, object level annotations, and could be used as part of an automated TB screening process.

We then present eRxNet, a pipeline of CNNs designed to provide healthcare professionals with detailed and accurate analysis of CXRs for TB screening. The pipeline combines whole image classification, object detection (bounding boxes), and instance segmentation (polygonal masks) to provide data analysis at varying levels of detail. In order to construct a high performing system, a comparison of different CNN architectures applied to these tasks is presented. Images from two large TB datasets, UML-Peru and TBX11K, were used for training and evaluation of the models. Combining the two datasets required the development of a preprocessing stage which includes lung segmentation and image enhancement. We show that the resulting four stage pipeline of CNNs, using a combination of DenseNet, Faster R-CNN, and Mask R-CNN, has sufficiently strong performance to be a useful tool for TB screening.

The eRx Repository is an online application to aid in the training of healthcare professionals in diagnosing TB. This tool provides access to the UML-Peru dataset, including expert annotations and selected results from eRxNet. It is hoped that this tool can help to address the shortage of trained professionals combating TB.

This work has the potential to improve the speed of TB diagnosis in LMICs, if properly integrated into the healthcare system and adapted to existing clinical workflows and local regulations.