Brain tumor classification on magnetic resonance imaging(MRI) scans using deep learning

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dc.contributor.authorMarumo, A.M.
dc.date.accessioned2023-11-15T09:30:40Z
dc.date.available2023-11-15T09:30:40Z
dc.date.issued2022
dc.descriptionA dissertation submitted in fulfilment of the requirements for the degree of Master of Science to the Faculty of Science, University of the Witwatersrand, Johannesburg, 2022
dc.description.abstractA brain tumor is formed when there is a development of aberrant cells in the brain. Early detection of brain tumors increases the patient’s chances of survival. This study proposes a Convolutional Neural Network(CNN) model or system that will automatically classify or detect brain tumors on MRI scans without the interference of radiologists or physicians. To make the proposed model trustworthy, integrated gradients and XRAI are built and evaluated. The CNN model achieved 90% accuracy, 82% sensitivity, 95% specificity, 82% precision, 79% Cohen’s kappa statistic, 79% Matthews correlation coefficient, and 77% Gini coefficient. The built classifier is best explained by integrated gradients. In the medical industry, integrated gradients haven’t been widely used as an explanation for deep learning models. This study demonstrates how integrated gradient can be used to interpret deep learning models in the medical area.
dc.description.librarianPC(2023)
dc.facultyFaculty of Science
dc.identifier.urihttps://hdl.handle.net/10539/36994
dc.language.isoen
dc.schoolComputer Science and Applied Mathematics
dc.subjectBrain tumor
dc.subjectMagnetic resonance imaging
dc.titleBrain tumor classification on magnetic resonance imaging(MRI) scans using deep learning
dc.typeDissertation
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