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dc.contributor.advisorJones, Edward
dc.contributor.authorOliveira, Bárbara Luz
dc.date.accessioned2019-02-22T08:30:19Z
dc.date.available2019-02-22T08:30:19Z
dc.date.issued2018-09-28
dc.identifier.urihttp://hdl.handle.net/10379/14986
dc.description.abstractToday, the early detection of breast cancer for asymptomatic women primarily relies on generalised screening programmes with x-ray mammography. However, the long-term value of screening mammography has been questioned due to e.g. the high false positive rate, resulting in unnecessary biopsies and overdiagnosed cancers. In this context, a need exists for new breast screening modalities with greater specificity. In this thesis, a machine learning platform using microwave technology is investigated for the purpose of diagnosing breast cancer. The proposed platform is evaluated by means of numerical and experimental phantom sets designed and developed in this research. The proposed numerical tumour phantom set is designed to ensure tumour models are clinically-realistic; a validation procedure was undertaken with clinicians to verify the applicability of the models. Experimental tumour and breast models were also developed using tissue mimicking materials. Both the level of spiculation in the tumour models and varying levels of glandular content were included as novel elements of the phantom set. Breast cancer diagnosis was investigated through the development of a 3-stage automated platform to analyse backscattered signals, which includes: data acquisition; data pre-processing through tumour windowing and feature extraction; and diagnosis through a random forests classifier in a two-level architecture. The results demonstrate the usefulness of creating high-similarity groups of signals before the classification and how the extraction of features can capture the characteristics of a tumour, without the need for a priori information. Results also show that benign tumours are more often correctly classified than malignant tumours, suggesting that microwave breast systems may be capable of achieving high specificity rates. In the context of current approaches to breast cancer care management, the results of this work support the potential value of microwave breast diagnosis systems to impact patient outcomes.en_IE
dc.publisherNUI Galway
dc.subjectMicrowave Breast Imagingen_IE
dc.subjectMachine Learningen_IE
dc.subjectBreast Cancer Diagnosisen_IE
dc.subjectEvaluation Phantomsen_IE
dc.subjectEngineering and Informaticsen_IE
dc.subjectElectrical and Electronic Engineeringen_IE
dc.titleTowards improved breast cancer diagnosis using microwave technology and machine learningen_IE
dc.typeThesisen
dc.contributor.funderScience Foundation Irelanden_IE
dc.local.noteThe work presented in this thesis concerns itself with the development of a platform to diagnose breast cancer, based on microwave breast signals. The automated microwave diagnosis platform was tested by means of numerical and experimental tumour models developed as an integral part of this work.en_IE
dc.local.finalYesen_IE
dcterms.projectinfo:eu-repo/grantAgreement/SFI/SFI Investigator Programme/12/IP/1523/IE/Breast Cancer detection and classification using Ultra Wideband Radar Tomography/en_IE
dcterms.projectinfo:eu-repo/grantAgreement/SFI/SFI Starting Investigator Research Grant (SIRG)/11/SIRG/I2120/IE/Microwave Imaging for the Detection and Classification of Early-Stage Breast Cancer/en_IE
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