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Proposal of the CAD System for Melanoma Detection Using Reconfigurable Computing

Wysterlânya K. P. Barros, Daniel S. Morais, Felipe F. Lopes, Matheus Torquato Orcid Logo, Raquel de M. Barbosa, Marcelo A. C. Fernandes

Sensors, Volume: 20, Issue: 11, Start page: 3168

Swansea University Author: Matheus Torquato Orcid Logo

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DOI (Published version): 10.3390/s20113168

Abstract

This work proposes dedicated hardware to real-time cancer detection using Field-Programmable Gate Arrays (FPGA). The presented hardware combines a Multilayer Perceptron (MLP) Artificial Neural Networks (ANN) with Digital Image Processing (DIP) techniques. The DIP techniques are used to extract the f...

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Published in: Sensors
ISSN: 1424-8220
Published: MDPI AG 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa54469
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first_indexed 2020-06-15T13:09:33Z
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spelling 2020-10-20T12:27:19.9627201 v2 54469 2020-06-15 Proposal of the CAD System for Melanoma Detection Using Reconfigurable Computing 7a053c668886b4642286baed36fdba90 0000-0001-6356-3538 Matheus Torquato Matheus Torquato true false 2020-06-15 SCS This work proposes dedicated hardware to real-time cancer detection using Field-Programmable Gate Arrays (FPGA). The presented hardware combines a Multilayer Perceptron (MLP) Artificial Neural Networks (ANN) with Digital Image Processing (DIP) techniques. The DIP techniques are used to extract the features from the analyzed skin, and the MLP classifies the lesion into melanoma or non-melanoma. The classification results are validated with an open-access database. Finally, analysis regarding execution time, hardware resources usage, and power consumption are performed. The results obtained through this analysis are then compared to an equivalent software implementation embedded in an ARM A9 microprocessor. Journal Article Sensors 20 11 3168 MDPI AG 1424-8220 artificial neural networks; digital image processing; melanoma detection 3 6 2020 2020-06-03 10.3390/s20113168 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2020-10-20T12:27:19.9627201 2020-06-15T09:57:40.6632745 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Wysterlânya K. P. Barros 1 Daniel S. Morais 2 Felipe F. Lopes 3 Matheus Torquato 0000-0001-6356-3538 4 Raquel de M. Barbosa 5 Marcelo A. C. Fernandes 6 54469__17492__4ed832e46e6248fbae35ecc4364d6154.pdf 54469.pdf 2020-06-15T09:59:07.9473734 Output 745592 application/pdf Version of Record true Released under the terms of a Creative Commons Attribution License (CC-BY). true English http://creativecommons.org/licenses/by/4.0/
title Proposal of the CAD System for Melanoma Detection Using Reconfigurable Computing
spellingShingle Proposal of the CAD System for Melanoma Detection Using Reconfigurable Computing
Matheus Torquato
title_short Proposal of the CAD System for Melanoma Detection Using Reconfigurable Computing
title_full Proposal of the CAD System for Melanoma Detection Using Reconfigurable Computing
title_fullStr Proposal of the CAD System for Melanoma Detection Using Reconfigurable Computing
title_full_unstemmed Proposal of the CAD System for Melanoma Detection Using Reconfigurable Computing
title_sort Proposal of the CAD System for Melanoma Detection Using Reconfigurable Computing
author_id_str_mv 7a053c668886b4642286baed36fdba90
author_id_fullname_str_mv 7a053c668886b4642286baed36fdba90_***_Matheus Torquato
author Matheus Torquato
author2 Wysterlânya K. P. Barros
Daniel S. Morais
Felipe F. Lopes
Matheus Torquato
Raquel de M. Barbosa
Marcelo A. C. Fernandes
format Journal article
container_title Sensors
container_volume 20
container_issue 11
container_start_page 3168
publishDate 2020
institution Swansea University
issn 1424-8220
doi_str_mv 10.3390/s20113168
publisher MDPI AG
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
document_store_str 1
active_str 0
description This work proposes dedicated hardware to real-time cancer detection using Field-Programmable Gate Arrays (FPGA). The presented hardware combines a Multilayer Perceptron (MLP) Artificial Neural Networks (ANN) with Digital Image Processing (DIP) techniques. The DIP techniques are used to extract the features from the analyzed skin, and the MLP classifies the lesion into melanoma or non-melanoma. The classification results are validated with an open-access database. Finally, analysis regarding execution time, hardware resources usage, and power consumption are performed. The results obtained through this analysis are then compared to an equivalent software implementation embedded in an ARM A9 microprocessor.
published_date 2020-06-03T04:08:01Z
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score 11.018934

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