Diagnosing brain tumours through functional imaging and machine learning

Zhao, Dadi Teddy ORCID: 0000-0001-7727-3638 (2023). Diagnosing brain tumours through functional imaging and machine learning. University of Birmingham. Ph.D.

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Non-invasive in vivo functional imaging plays a key role in the clinical management of brain tumours. Magnetic resonance spectroscopy estimates the metabolite profiles of brain tissues. Diffusion weighted imaging shows the water diffusion map of the whole brain. Such imaging biomarkers facilitate the precise classification of brain tumours. Prior to classification, this the- sis optimised image and spectroscopy preprocessing and feature selection. Magnetic resonance spectroscopy was processed through adaptive wavelet noise suppression. Imaging features were selected based on the diagnostic ability across all tumour types. Additionally, imaging features of the minority class were oversampled through semi-synthetic wavelets oversampling. Combining these techniques, the cross validated classification accuracy for ependymomas, medulloblastomas and pilocytic astrocytomas was dramatically improved to be 100%. The findings from this thesis enhanced the role of magnetic resonance spectroscopy in clinical neuroscience.

Type of Work: Thesis (Doctorates > Ph.D.)
Award Type: Doctorates > Ph.D.
Peet, Andrew CharlesUNSPECIFIEDorcid.org/0000-0002-4846-5152
Licence: All rights reserved
College/Faculty: Colleges (2008 onwards) > College of Medical & Dental Sciences
School or Department: Institute of Cancer and Genomic Sciences
Funders: Cancer Research UK
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
URI: http://etheses.bham.ac.uk/id/eprint/13042


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