Classifying Breast Tumors using Medical Microwave Radar Imaging

• Autor(a) principal: Rodrigues, Miguel Ângelo Borlão
• Data de Publicação: 2021
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10362/126696
• topic: Breast Cancer; Medical Microwave Imaging; UWB Microwave Radar Imaging System; MRI Segmentation; Numerical Models; Classification Algorithms.; Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Médica

Medical Microwave Imaging (MMI) has been studied in the past years to develop techniques to detect breast cancer at the earliest stages of development. Particularly, ultra-wideband (UWB) micro-wave radar imaging systems can detect and classify tumors as benign or malignant since this technique yields information about the size and shape of tumors. In this study we used this technology to classify tumors. The primary goal of this dissertation is two-folded. First, producing breast tumor numerical mod-els and using them in 2D MMI simulations that recreate the conditions of a UWB microwave radar imaging system. The breast tumor numerical produced resemble real tumor morphologies since they are made from breast MRI exams segmentations. Second, the data of the backscattered UWB microwave signals produced by the MMI simulations was used to classify tumors according to their size and histol-ogy, which is relevant to assess potential of UWB microwave radar imaging systems as a reliable alter-native method for the classification of breast tumors in the field of Medical Microwave Imaging. The Classification Algorithms used in this work were Pseudo Linear Discriminant Analysis (Pseudo-LDA), Pseudo Quadratic Discriminant Analysis (pseudo-QDA), and k-Nearest Neighbors (KNN), alongside with a feature extraction algorithm – Principal Component Analysis (PCA).