Melanoma skin cancer recognition using negative selection algorithm / Muhammad Rushamir Hakimi Ruslan

This project presents a novel intelligence that inspired by immune system or specifically the Artificial Immune System. The Negative Selection Algorithm has been successfully applied in several application areas such as fault detection, virus detection and data integrity protection. This study propo...

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Main Author:	Ruslan, Muhammad Rushamir Hakimi
Format:	Thesis
Language:	English
Published:	2017
Subjects:	Instruments and machines Computer software Application software Software measurement Neural networks (Computer science) Algorithms Database management
Online Access:	https://ir.uitm.edu.my/id/eprint/69410/1/69410.pdf
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id	my-uitm-ir.69410
record_format	uketd_dc
spelling	my-uitm-ir.694102024-02-29T02:15:50Z Melanoma skin cancer recognition using negative selection algorithm / Muhammad Rushamir Hakimi Ruslan 2017-01 Ruslan, Muhammad Rushamir Hakimi Instruments and machines Electronic Computers. Computer Science Evolutionary programming (Computer science). Genetic algorithms Computer software Application software Development. UML (Computer science) Software measurement Neural networks (Computer science) Algorithms Database management This project presents a novel intelligence that inspired by immune system or specifically the Artificial Immune System. The Negative Selection Algorithm has been successfully applied in several application areas such as fault detection, virus detection and data integrity protection. This study proposed and focused on the development of a prototype that uses the Negative Selection Algorithm to classify the input image whether it is belongs to melanoma skin cancer or benign mole. The criteria of the skin image that takes into account are Asymmetric Index, Border Irregularity, Color Invariant and Diameter of the lesion. This technique inspired by the ABCD rule where it is adopted as the standard rule to diagnose the skin cancer. This study has shown how the Negative Selection Algorithm can diagnose the skin cancer based on the input image and extracted data that has been provided. This study has been conducted with 30 data which are the skin images is divided into 20 training and 10 testing data for the proposed algorithm. The result of the evaluation analysis conducted in this study shown that accuracy of the result is 60%, the specificity obtained is 75% and sensitivity obtained is 50%. Hence, the proposed algorithm is capable to classify the skin image whether it is melanoma or benign mole based on the given data. For future enhancement of the prototype such as enhance the features extraction technique and hybrid the existing algorithm with another AIS algorithm can be conducted in order to obtain higher accuracy of the result gained. 2017-01 Thesis https://ir.uitm.edu.my/id/eprint/69410/ https://ir.uitm.edu.my/id/eprint/69410/1/69410.pdf text en public degree Universiti Teknologi MARA, Terengganu Faculty of Computer and Mathematical Sciences Sa’dan, Siti ‘Aisyah
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Sa’dan, Siti ‘Aisyah
topic	Instruments and machines Instruments and machines Instruments and machines Computer software Application software Instruments and machines Software measurement Neural networks (Computer science) Algorithms Database management
spellingShingle	Instruments and machines Instruments and machines Instruments and machines Computer software Application software Instruments and machines Software measurement Neural networks (Computer science) Algorithms Database management Ruslan, Muhammad Rushamir Hakimi Melanoma skin cancer recognition using negative selection algorithm / Muhammad Rushamir Hakimi Ruslan
description	This project presents a novel intelligence that inspired by immune system or specifically the Artificial Immune System. The Negative Selection Algorithm has been successfully applied in several application areas such as fault detection, virus detection and data integrity protection. This study proposed and focused on the development of a prototype that uses the Negative Selection Algorithm to classify the input image whether it is belongs to melanoma skin cancer or benign mole. The criteria of the skin image that takes into account are Asymmetric Index, Border Irregularity, Color Invariant and Diameter of the lesion. This technique inspired by the ABCD rule where it is adopted as the standard rule to diagnose the skin cancer. This study has shown how the Negative Selection Algorithm can diagnose the skin cancer based on the input image and extracted data that has been provided. This study has been conducted with 30 data which are the skin images is divided into 20 training and 10 testing data for the proposed algorithm. The result of the evaluation analysis conducted in this study shown that accuracy of the result is 60%, the specificity obtained is 75% and sensitivity obtained is 50%. Hence, the proposed algorithm is capable to classify the skin image whether it is melanoma or benign mole based on the given data. For future enhancement of the prototype such as enhance the features extraction technique and hybrid the existing algorithm with another AIS algorithm can be conducted in order to obtain higher accuracy of the result gained.
format	Thesis
qualification_level	Bachelor degree
author	Ruslan, Muhammad Rushamir Hakimi
author_facet	Ruslan, Muhammad Rushamir Hakimi
author_sort	Ruslan, Muhammad Rushamir Hakimi
title	Melanoma skin cancer recognition using negative selection algorithm / Muhammad Rushamir Hakimi Ruslan
title_short	Melanoma skin cancer recognition using negative selection algorithm / Muhammad Rushamir Hakimi Ruslan
title_full	Melanoma skin cancer recognition using negative selection algorithm / Muhammad Rushamir Hakimi Ruslan
title_fullStr	Melanoma skin cancer recognition using negative selection algorithm / Muhammad Rushamir Hakimi Ruslan
title_full_unstemmed	Melanoma skin cancer recognition using negative selection algorithm / Muhammad Rushamir Hakimi Ruslan
title_sort	melanoma skin cancer recognition using negative selection algorithm / muhammad rushamir hakimi ruslan
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2017
url	https://ir.uitm.edu.my/id/eprint/69410/1/69410.pdf
_version_	1794191838673895424

Melanoma skin cancer recognition using negative selection algorithm / Muhammad Rushamir Hakimi Ruslan

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