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

dc.contributor.advisorViriri, Serestina.
dc.contributor.authorOloko-Oba, Mustapha Olayemi.
dc.date.accessioned2022-07-05T07:52:12Z
dc.date.available2022-07-05T07:52:12Z
dc.date.created2022
dc.date.issued2022
dc.descriptionDoctoral Degree. University of KwaZulu-Natal, Durban.en_US
dc.description.abstractTuberculosis (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.en_US
dc.description.notesAuthor's Publications listed on page iii.en_US
dc.identifier.urihttps://researchspace.ukzn.ac.za/handle/10413/20599
dc.language.isoenen_US
dc.subject.otherRadiography--Chest.en_US
dc.subject.otherTuberculosis--Developing countries.en_US
dc.subject.otherDeep learning--Tuberculosis.en_US
dc.subject.otherComputer-aided design.en_US
dc.subject.otherComputer-aided diagnostic--TB.en_US
dc.subject.otherMachine learning--Tuberculosis.en_US
dc.subject.otherEnsemble methods--TB.en_US
dc.titleTuberculosis diagnosis from pulmonary chest x-ray using deep learning.en_US
dc.typeThesisen_US

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