Tuberculosis diagnosis from pulmonary chest x-ray using deep learning.
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Date
2022
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Abstract
Tuberculosis (TB) remains a life-threatening disease, and it is one of the leading
causes of mortality in developing countries. This is due to poverty and inadequate
medical resources. While treatment for TB is possible, it requires an accurate diagnosis
first. Several screening tools are available, and the most reliable is Chest
X-Ray (CXR), but the radiological expertise for accurately interpreting the CXR
images is often lacking. Over the years, CXR has been manually examined; this
process results in delayed diagnosis, is time-consuming, expensive, and is prone
to misdiagnosis, which could further spread the disease among individuals. Consequently,
an algorithm could increase diagnosis efficiency, improve performance,
reduce the cost of manual screening and ultimately result in early/timely diagnosis.
Several algorithms have been implemented to diagnose TB automatically. However,
these algorithms are characterized by low accuracy and sensitivity leading to misdiagnosis.
In recent years, Convolutional Neural Networks (CNN), a class of Deep
Learning, has demonstrated tremendous success in object detection and image classification
task. Hence, this thesis proposed an efficient Computer-Aided Diagnosis
(CAD) system with high accuracy and sensitivity for TB detection and classification.
The proposed model is based firstly on novel end-to-end CNN architecture,
then a pre-trained Deep CNN model that is fine-tuned and employed as a features
extractor from CXR. Finally, Ensemble Learning was explored to develop an
Ensemble model for TB classification. The Ensemble model achieved a new stateof-
the-art diagnosis accuracy of 97.44% with a 99.18% sensitivity, 96.21% specificity
and 0.96% AUC. These results are comparable with state-of-the-art techniques and
outperform existing TB classification models.
Description
Doctoral Degree. University of KwaZulu-Natal, Durban.