Scattering performance verification based on UWB imaging and neural network
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 expe...
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Format: | Conference or Workshop Item |
Language: | English English |
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IEEE
2017
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Online Access: | http://umpir.ump.edu.my/id/eprint/28995/1/Scattering%20performance%20verification%20based%20on%20UWB%20imaging_FULL.pdf http://umpir.ump.edu.my/id/eprint/28995/2/Scattering%20performance%20verification%20based%20on%20UWB%20imaging.pdf |
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author | V., Vijayasarveswari Muzammir, Jusoh Sabira, Khatun Fakir, Md Moslemuddin |
author_facet | V., Vijayasarveswari Muzammir, Jusoh Sabira, Khatun Fakir, Md Moslemuddin |
author_sort | V., Vijayasarveswari |
collection | UMP |
description | 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. |
first_indexed | 2024-03-06T12:44:12Z |
format | Conference or Workshop Item |
id | UMPir28995 |
institution | Universiti Malaysia Pahang |
language | English English |
last_indexed | 2024-03-06T12:44:12Z |
publishDate | 2017 |
publisher | IEEE |
record_format | dspace |
spelling | UMPir289952022-08-19T08:29:27Z http://umpir.ump.edu.my/id/eprint/28995/ Scattering performance verification based on UWB imaging and neural network V., Vijayasarveswari Muzammir, Jusoh Sabira, Khatun Fakir, Md Moslemuddin QA76 Computer software TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering 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. IEEE 2017-10-10 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/28995/1/Scattering%20performance%20verification%20based%20on%20UWB%20imaging_FULL.pdf pdf en http://umpir.ump.edu.my/id/eprint/28995/2/Scattering%20performance%20verification%20based%20on%20UWB%20imaging.pdf 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 https://doi.org/10.1109/CSPA.2017.8064958 |
spellingShingle | QA76 Computer software TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering V., Vijayasarveswari Muzammir, Jusoh Sabira, Khatun Fakir, Md Moslemuddin Scattering performance verification based on UWB imaging and neural network |
title | Scattering performance verification based on UWB imaging and neural network |
title_full | Scattering performance verification based on UWB imaging and neural network |
title_fullStr | Scattering performance verification based on UWB imaging and neural network |
title_full_unstemmed | Scattering performance verification based on UWB imaging and neural network |
title_short | Scattering performance verification based on UWB imaging and neural network |
title_sort | scattering performance verification based on uwb imaging and neural network |
topic | QA76 Computer software TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering |
url | http://umpir.ump.edu.my/id/eprint/28995/1/Scattering%20performance%20verification%20based%20on%20UWB%20imaging_FULL.pdf http://umpir.ump.edu.my/id/eprint/28995/2/Scattering%20performance%20verification%20based%20on%20UWB%20imaging.pdf |
work_keys_str_mv | AT vvijayasarveswari scatteringperformanceverificationbasedonuwbimagingandneuralnetwork AT muzammirjusoh scatteringperformanceverificationbasedonuwbimagingandneuralnetwork AT sabirakhatun scatteringperformanceverificationbasedonuwbimagingandneuralnetwork AT fakirmdmoslemuddin scatteringperformanceverificationbasedonuwbimagingandneuralnetwork |