Scattering performance verification based on UWB imaging and neural network

V., Vijayasarveswari and Muzammir, Jusoh and Sabira, Khatun and Fakir, Md Moslemuddin (2017) Scattering performance verification based on UWB imaging and neural network. In: 13th IEEE International Colloquium on Signal Processing and its Applications, CSPA 2017, 10 - 12 March 2017 , Batu Ferringhi Beach, Penang. pp. 238-242. (8064958). ISBN 9781509011841

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Breast cancer cases are increasing year by year and second leading reasons for the women's death worldwide. Early detection is very important and will help to save thousands of peoples' lives. The available systems such as Mammogram, MRI and ultrasound are invasive, expensive and need expert to operate. This paper presents a low cost and non-invasive breast cancer detection system for early detection. This system consisted hardware which consist a pair of home-made antenna and Ultra wide-band (UWB) and software which consist of a Neural Network (NN) module. Antenna will transmit the signal while another will receive. Both forward scattering and backward scattering performance are analyzed. The received signals are fed into NN module for further processing. Breast phantom is placed in the center and a pair of home-made antennas was placed diagonally opposite side of the breast phantom. K-fold cross validation based feed forward NN is used to train, validate and test the features. The system can screen the breast cancer with average detection performance of 87.55% using backward scattering signals while 84.17% using forward scattering signal. The proposed breast cancer detection system will be very useful for home user to check breast health regularly.

Item Type: Conference or Workshop Item (Lecture)
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Breast cancer detection; UWB; Forward scattered signal; Backward scattered signal; Neural network
Subjects: Q Science > QA Mathematics > QA76 Computer software
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Computer System And Software Engineering
Faculty of Electrical & Electronic Engineering
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 19 Aug 2022 08:29
Last Modified: 19 Aug 2022 08:29
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