Tuberculosis diagnosis from pulmonary chest x-ray using deep learning.

Doctoral Degree. University of KwaZulu-Natal, Durban.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.Author's Publications listed on page iii

Advanced MRI methods for probing disease severity and functional decline in multiple sclerosis

Multiple sclerosis (MS) is a chronic and severe disease of the central nervous system characterized by complex pathology including inflammatory demyelination and neurodegeneration. MS impacts >2.8 million people worldwide, with most starting with a relapsing-remitting form (RRMS) in young adulthood, and many of them worsening to a secondary-progressive course (SPMS) despite treatment. So, there is a clear need for improved disease characterization. MRI is an ideal tool for non-invasive assessment of MS pathology, but there is still no established measure of disease activity and functional consequences. This project aims to overcome the challenge by developing novel imaging measures based on brain diffusion MRI and phase congruency texture analysis of conventional MRI. Through advanced modeling and analysis of clinically feasible brain MRI, this thesis investigates whether and how the derived measures differentiate MS pathology types and disease severity and predict functional outcomes in MS. The overall process has led to important technical innovations in several aspects. These include: innovative modeling of simple diffusion acquisitions to generate high angular resolution diffusion imaging (HARDI) measures; new optimization and harmonization techniques for diffusion MRI; innovative neural network models to create new diffusion data for comprehensive HARDI modeling; and novel methods and a graphic user interface for optimizing phase congruency analyses. Assisted by different machine learning methods, collective findings show that advanced measures from both diffusion MRI and phase congruency are highly sensitive to subtle differences in MS pathology, which differentiate disease severity between RRMS and SPMS through multi-dimensional analyses including chronic active lesions, and predict functional outcomes especially in physical and neurocognitive domains. These results are clinically translational and the new measures and techniques can help improve the evaluation and management of both MS and similar diseases

19F Magnetic Resonance Imaging of Lung Ventilation Dynamics and Cell Tracking

The Fluorine isotope 19F has great potential in the use of magnetic resonance imaging (MRI) for clinical applications. 19F is inert, naturally abundant, has a close resonance frequency to proton (1H) (allowing most modern MRI scanners to work with the addition of a tuned coil), has negligible presence in the mammalian body (allowing background signal free acquisitions), and the high gyromagnetic ratio provides sufficient magnetic resonance signal to be visible without hyperpolarization. Uses for 19F MRI includes functional lung imaging, diffusion imaging, cell tracking, and oxygenation sensing among others. Although not widely used in the clinical setting at the time of writing this dissertation. The potential improvements 19F MRI could bring to healthcare are vast. 19F lung imaging has been studied in animal and human models, and has shown to be capable of producing sensitive markers for lung diseases such as cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD) by providing spatially localized functional information. In cell tracking, 19F has shown potential in drug delivery monitoring, inflammation imaging, immune cell tracking, and oxygenation measurement with the potential of spatial localization and cell quantification. This dissertation presents my work on human in-vivo multi-breath wash-in/out 19F lung imaging, and the processing of biomarkers more sensitive to CF disease progression over the current gold standard (spirometry). 19F lung MRI was compared to hyperpolarized (HP) Xenon (129Xe) ventilation defect percentage (VDP) analysis. The feasibility of free-breathing 19F lung imaging was explored using a combination of spiral acquisition and denoising. The last two chapters present preliminary work on sequence programming for diffusion imaging and cell tracking at high magnetic fields (9.4T). Preliminary work on oxygen sensing at 9.4T is also explored.Doctor of Philosoph

Health outcomes in ankylosing spondylitis : an evaluation of patient-based and anthropometric measures.

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