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Ultra Wide Band (UWB) Based Early Breast Cancer Detection Using Artificial Intelligence

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  • First Online:
InECCE2019

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 632))

Abstract

Breast cancer is a silent killer malady among women community all over the world. The death rate is increased as it has no syndrome at an early stage. There is no remedy; hence, detection at the early stage is crucial. Usually, women do not go to clinic/hospital for regular breast health checkup unless they are sick. This is due to the long queue and waiting time in the hospital, high cost, people’s busy schedule, and so on. Recently, several research works have been done on early breast cancer detection using Ultra Wide Band (UWB) technology because of its non-invasive and health-friendly nature. Each proposed UWB system has its limitation including system complexity, expensive, expert operable in the clinic. To overcome these problems, a system is required which should be simple, cost-effective and user-friendly. This chapter presents the development of a user friendly and affordable UWB system for early breast cancer detection utilizing Artificial Neural Network (ANN). A feed-forward back propagation Neural Network (NN) with ‘feedforwardnet’ function is utilized to detect the cancer existence, size as well as the location in 3-dimension (3D). The hardware incorporates UWB transceiver and a pair of pyramidal shaped patch antenna to transmit and receive the UWB signals. The extracted features from the received signals were fed into the NN module to train, validate, and test. The average system’s performance efficiency in terms of tumor/cancer existence, size and location is approximately 100%, 92.43%, and 91.31% respectively. Here, in our system, use of ‘feedforwardnet’ function; detection-combination of tumor/cancer existence, size and location in 3D along with improved performance is a new addition compared to other related researches and/or existing systems. This may become a promising user-friendly system in the near future for early breast cancer detection in a domestic environment with low cost and to save precious human life.

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Acknowledgements

This work is supported by Universiti Malaysia Pahang (UMP), Internal Research Grant RDU1703125 and UMP Post-Graduate Research Scheme (PGRS190327). The authors would like to thank the Faculty of Electrical & Electronics Engineering (FKEE), UMP (https://www.ump.edu.my) for providing the facilities to conduct this work and for financial support throughout the process.

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Authors and Affiliations

  1. Faculty of Electrical and Electronics Engineering, Universiti Malaysia Pahang, Pekan, Malaysia

    Bifta Sama Bari, Sabira Khatun, Kamarul Hawari Ghazali, Wan Nur Azhani W. Samsudin, Mohd Falfazli Mat Jusof, Mamunur Rashid, Minarul Islam & Mohd Zamri Ibrahim

  2. CARIFF, Faculty of Chemical and Natural Resources Engineering, Universiti Malaysia Pahang, Pekan, Malaysia

    Md. Moslemuddin Fakir

Authors
  1. Bifta Sama Bari
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  2. Sabira Khatun
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  3. Kamarul Hawari Ghazali
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  4. Md. Moslemuddin Fakir
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  5. Wan Nur Azhani W. Samsudin
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  6. Mohd Falfazli Mat Jusof
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  7. Mamunur Rashid
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  8. Minarul Islam
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  9. Mohd Zamri Ibrahim
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Corresponding author

Correspondence to Sabira Khatun .

Editor information

Editors and Affiliations

  1. Faculty of Electrical and Electronics Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Ahmad Nor Kasruddin Nasir

  2. Faculty of Electrical and Electronics Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Mohd Ashraf Ahmad

  3. Faculty of Electrical and Electronics Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Muhammad Sharfi Najib

  4. Faculty of Electrical and Electronics Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Yasmin Abdul Wahab

  5. Faculty of Electrical and Electronics Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Nur Aqilah Othman

  6. Faculty of Electrical and Electronics Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Nor Maniha Abd Ghani

  7. Faculty of Electrical and Electronics Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Addie Irawan

  8. Faculty of Electrical and Electronics Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Sabira Khatun

  9. Faculty of Electrical and Electronics Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Raja Mohd Taufika Raja Ismail

  10. Faculty of Electrical and Electronics Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Mohd Mawardi Saari

  11. Faculty of Electrical and Electronics Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Mohd Razali Daud

  12. Faculty of Electrical and Electronics Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Ahmad Afif Mohd Faudzi

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Bari, B.S. et al. (2020). Ultra Wide Band (UWB) Based Early Breast Cancer Detection Using Artificial Intelligence. In: Kasruddin Nasir, A.N., et al. InECCE2019. Lecture Notes in Electrical Engineering, vol 632. Springer, Singapore. https://doi.org/10.1007/978-981-15-2317-5_43

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  • DOI: https://doi.org/10.1007/978-981-15-2317-5_43

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-2316-8

  • Online ISBN: 978-981-15-2317-5

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