Diagnosing Breast Cancer with Microwave Technology: Remaining Challenges and Potential Solutions with Machine Learning

Abstract

Currently, breast cancer often requires invasive biopsies for diagnosis, motivating researchers to design and develop non-invasive and automated diagnosis systems. Recent microwave breast imaging studies have shown how backscattered signals carry relevant information about the shape of a tumour, and tumour shape is often used with current imaging modalities to assess malignancy. This paper presents a comprehensive analysis of microwave breast diagnosis systems which use machine learning to learn characteristics of benign and malignant tumours. The state-of-the-art, the main challenges still to overcome and potential solutions are outlined. Specifically, this work investigates the benefit of signal pre-processing on diagnostic performance, and proposes a new set of extracted features that capture the tumour shape information embedded in a signal. This work also investigates if a relationship exists between the antenna topology in a microwave system and diagnostic performance. Finally, a careful machine learning validation methodology is implemented to guarantee the robustness of the results and the accuracy of performance evaluation.

Brain Haemorrhage Detection Through SVM Classification of Electrical Impedance Tomography Measurements

“…SVMs are a group of popular ML algorithms commonly employed for binary classification. They have been used in previous biomedical applications, including the use of microwave signals to classify whether a breast scan is considered healthy or tumourous [24][25][26], and electrical impedance spectroscopy signals for classification of breast [27][28][29] and prostate [30] as diseased or normal. The use of EIT measurements in ML algorithms is a relatively new area of research.…”

“…This procedure is then repeated for all five of the unique training-testing data pairs, and final classifier performance is presented as the mean and standard deviation (STD) across these five iterations. This nested testing methodology, which has been used previously in the literature [26,53], provides a more generalised and robust indication of classifier performance.…”

Introduction to Special Issue on “Electromagnetic Technologies for Medical Diagnostics: Fundamental Issues, Clinical Applications and Perspectives”

“…One of the most popular medical applications investigated by microwave researchers is breast cancer detection, and this special issue includes three papers on this topic. First, Oliveira et al present an interesting overview of applying machine learning algorithms as a way to distinguish between benign and malignant tumours, using ultra-wideband (UWB) radar data [14]. UWB imaging is also presented in [15], which succeeds in comparing breast images obtained from patients using UWB radar with X-ray mammography.…”

Classification of breast tumor models with a prototype microwave imaging system

“…These studies were expanded and tested in numerical homogeneous and heterogeneous breast model scenarios. 10 Oliveira et al 21 completed a clinically realistic dataset of numerical models of the breast and breast tumors. In this paper, a pipeline comprising a tumor windowing step, appropriate FEMs (a set of features that represent the signal morphology and frequency information from tumors), and a machine learning algorithm -Random Forestswas implemented.…”