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

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.

[1]  Amin M. Abbosh,et al.  Experimental assessment of microwave diagnostic tool for ultra-wideband breast cancer detection , 2012 .

[2]  Milica Popovic,et al.  Investigation of Classifiers for Tumor Detection with an Experimental Time-Domain Breast Screening System , 2014 .

[3]  Rozi Mahmud,et al.  A UWB imaging system to detect early breast cancer in heterogeneous breast phantom , 2011, International Conference on Electrical, Control and Computer Engineering 2011 (InECCE).

[4]  Sabira Khatun,et al.  UWB imaging for breast cancer detection using neural network. , 2009 .

[5]  Sun Young Min,et al.  Basic Facts of Breast Cancer in Korea in 2014: The 10-Year Overall Survival Progress , 2017, Journal of breast cancer.

[6]  Sabira Khatun,et al.  Homogeneous and heterogeneous breast phantoms for UWB imaging , 2011, ISABEL '11.

[7]  P.M. Meaney,et al.  An active microwave imaging system for reconstruction of 2-D electrical property distributions , 1995, IEEE Transactions on Biomedical Engineering.

[8]  Berkman Sahiner,et al.  Breast masses: computer-aided diagnosis with serial mammograms. , 2006, Radiology.

[9]  Timothy J Wilt,et al.  Screening for breast cancer: U.S. Preventive Services Task Force recommendation statement. , 2009, Annals of internal medicine.

[10]  Edward Jones,et al.  Investigation of Classifiers for Early-Stage Breast Cancer Based on Radar Target Signatures , 2010 .

[11]  Raja Syamsul Azmir Raja Abdullah,et al.  3D experimental detection and discrimination of malignant and benign breast tumor using NN-based UWB imaging system , 2011 .

[12]  Zulkarnay Zakaria,et al.  Non-Invasive Breast Cancer Assessment Using Magnetic Induction Spectroscopy Technique , 2017 .

[13]  J. M. Sill,et al.  Preliminary investigations of tissue sensing adaptive radar for breast tumor detection , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[14]  S. Khatun,et al.  Experimental UWB based Efficient Breast Cancer Early Detection , 2017 .

[15]  Timothy J Wilt,et al.  Screening for breast cancer: U.S. Preventive Services Task Force recommendation statement. , 2009, Annals of internal medicine.

[16]  Seungjun Lee,et al.  Recent Advances in Microwave Imaging for Breast Cancer Detection , 2016, Int. J. Biomed. Imaging.

[17]  Raja Syamsul Azmir Raja Abdullah,et al.  Experimental Breast Tumor Detection Using Nn-Based UWB Imaging , 2011 .

[18]  S D Stellman,et al.  A different perspective on breast cancer risk factors: Some implications of the nonattributable risk , 1982, CA: a cancer journal for clinicians.

[19]  E. Madsen,et al.  Tissue-mimicking phantom materials for narrowband and ultrawideband microwave applications , 2005, Physics in medicine and biology.

[20]  M. H. Misran,et al.  Microwave Imaging Technique Using UWB Signal For Breast Cancer Detection , 2015 .

[21]  Martin Glavin,et al.  A multistage selective weighting method for improved microwave breast tomography , 2016, Comput. Medical Imaging Graph..

[22]  V. Vijayasarveswari,et al.  UWB based low-cost and non-invasive practical breast cancer early detection , 2017 .

[23]  A R Padhani,et al.  Cost-effectiveness of screening with contrast enhanced magnetic resonance imaging vs X-ray mammography of women at a high familial risk of breast cancer , 2006, British Journal of Cancer.

[24]  Z. A. Ahmad,et al.  Performance verification on UWB antennas for breast cancer detection , 2017 .