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

Journal article

Rana, S., Dey, M, Tiberi, G, Sani, L, Vispa, A, Raspa, G, Duranti, M, Ghavami, M and Dudley, S (2019). Machine Learning Approaches for Automated Lesion Detection in Microwave Breast Imaging Clinical Data. Scientific Reports. 9, p. 10510. https://doi.org/10.1038/s41598-019-46974-3
AuthorsRana, S., Dey, M, Tiberi, G, Sani, L, Vispa, A, Raspa, G, Duranti, M, Ghavami, M and Dudley, S
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.

KeywordsMachine Learning; Lesion Detection; Microwave Breast Imaging
Year2019
JournalScientific Reports
Journal citation9, p. 10510
PublisherSpringer Nature Publishing
Digital Object Identifier (DOI)https://doi.org/10.1038/s41598-019-46974-3
Web address (URL)https://www.nature.com/articles/s41598-019-46974-3
Publication dates
Print19 Jul 2019
Publication process dates
Deposited08 Jul 2019
Accepted04 Jul 2019
Accepted author manuscript
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Open
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Liao, Z, Li, Y, Peng, Y, Zhao, Y, Zhou, F, Liao, Z, Dudley, S and Ghavami, M (2015). A semantic-enhanced trajectory visual analytics for digital forensic. Journal of Visualization. 18 (2), pp. 173 - 184. https://doi.org/10.1007/s12650-015-0276-z
Classification of awake, REM, and NREM from EEG via singular spectrum analysis
Mohammadi, SM, Enshaeifar, S, Ghavami, M and Sanei, S (2015). Classification of awake, REM, and NREM from EEG via singular spectrum analysis. International Conference of the IEEE Engineering in Medicine and Biology Society. Milan, Italy 25 - 29 Aug 2015 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/EMBC.2015.7319460
Automatic trust calculation for service-oriented systems.
Ye, B, Nekovee, M, Pervez, A and Ghavami, M (2014). Automatic trust calculation for service-oriented systems. IET Software. 8 (3), pp. 134 - 142. https://doi.org/10.1049/iet-sen.2013.0056
Ultra-wideband-based multilateration technique for indoor localisation
Onalaja, O, Adjrad, M and Ghavami, M (2014). Ultra-wideband-based multilateration technique for indoor localisation. IET COMMUNICATIONS. 8 (10), pp. 1800 - 1809. https://doi.org/10.1049/iet-com.2013.0815
Study of human being detection in an indoor environment using ultra wideband radar
Hassan Khausru, MA and Ghavami, M (2014). Study of human being detection in an indoor environment using ultra wideband radar. IEEE International Conference on Ultra-Wideband 2014. Paris, France Institute of Electrical and Electronics Engineers (IEEE). pp. 113 - 118 https://doi.org/10.1109/ICUWB.2014.6958961
Signal Detection Performance of Overlapped FFT Scheme with Additional Frames Consisting of Non-continuous Samples in Indoor Environment.
Uchida, S, Sato, A, Inamori, M, Sanada, Y and Ghavami, M (2014). Signal Detection Performance of Overlapped FFT Scheme with Additional Frames Consisting of Non-continuous Samples in Indoor Environment. Wireless Personal Communications. 79 (2), pp. 987 - 1002. https://doi.org/10.1007/s11277-014-1899-4
A user-centric system architecture for residential energy consumption reduction
Vastardis, N, Adjrad, M, Buchanan, K, Liao, Z, Koch, C, Russo, R, Yang, K, Ghavami, M, Anderson, B and Dudley, S (2014). A user-centric system architecture for residential energy consumption reduction. IEEE Online Conference on Green Communications. Online 12 - 14 Nov 2014 Institute of Electrical and Electronics Engineers (IEEE). pp. 1-7 https://doi.org/10.1109/OnlineGreenCom.2014.7114423