Machine Learning Approaches for Automated Lesion Detection in Microwave Breast Imaging Clinical Data.

Journal: Scientific reports
PMID: 31324863

Abstract

Breast lesion detection employing state of the art microwave systems provide a safe, non-ionizing technique that can differentiate healthy and non-healthy tissues by exploiting their dielectric properties. In this paper, a microwave apparatus for breast lesion detection is used to accumulate clinical data from subjects undergoing breast examinations at the Department of Diagnostic Imaging, Perugia Hospital, Perugia, Italy. This paper presents the first ever clinical demonstration and comparison of a microwave ultra-wideband (UWB) device augmented by machine learning with subjects who are simultaneously undergoing conventional breast examinations. Non-ionizing microwave signals are transmitted through the breast tissue and the scattering parameters (S-parameter) are received via a dedicated moving transmitting and receiving antenna set-up. The output of a parallel radiologist study for the same subjects, performed using conventional techniques, is taken to pre-process microwave data and create suitable data for the machine intelligence system. These data are used to train and investigate several suitable supervised machine learning algorithms nearest neighbour (NN), multi-layer perceptron (MLP) neural network, and support vector machine (SVM) to create an intelligent classification system towards supporting clinicians to recognise breasts with lesions. The results are rigorously analysed, validated through statistical measurements, and found the quadratic kernel of SVM can classify the breast data with 98% accuracy.

Authors

  • Soumya Prakash Rana
    Division of Electrical and Electronic Engineering, School of Engineering, London South Bank University, London, United Kingdom. ranas9@lsbu.ac.uk.
  • Maitreyee Dey
    Division of Electrical and Electronic Engineering, School of Engineering, London South Bank University, London, United Kingdom.
  • Gianluigi Tiberi
    Division of Electrical and Electronic Engineering, School of Engineering, London South Bank University, London, United Kingdom.
  • Lorenzo Sani
    UBT Srl, Spin Off of the University of Perugia, Perugia, Italy.
  • Alessandro Vispa
    UBT Srl, Spin Off of the University of Perugia, Perugia, Italy.
  • Giovanni Raspa
    UBT Srl, Spin Off of the University of Perugia, Perugia, Italy.
  • Michele Duranti
    Department of Diagnostic Imaging, Perugia Hospital, Perugia, Italy.
  • Mohammad Ghavami
    Division of Electrical and Electronic Engineering, School of Engineering, London South Bank University, London, United Kingdom.
  • Sandra Dudley
    Division of Electrical and Electronic Engineering, School of Engineering, London South Bank University, London, United Kingdom.

Keywords

External Resources

Popular Topics
Recent Journals