Computer-aided detection of Melanoma using geometric features

Melanoma is one type of skin cancer that usually develops from prolonged exposure to UV light. The latter triggers mutations that lead skin cells to multiply rapidly and form malignant tumors. If not cured, Melanoma can result in one's death. Hence, an early detection of this deadly cancer is i...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Azar, Danielle (author)
مؤلفون آخرون: Moussa, Rebecca (author), Gerges, Firas (author), Salem, Christian (author), Akiki, Romario (author), Falou, Omar (author)
التنسيق: conferenceObject
منشور في: 2017
الوصول للمادة أونلاين:http://hdl.handle.net/10725/5368
http://dx.doi.org/10.1109/MECBME.2016.7745423
http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php
http://ieeexplore.ieee.org/abstract/document/7745423/
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author Azar, Danielle
author2 Moussa, Rebecca
Gerges, Firas
Salem, Christian
Akiki, Romario
Falou, Omar
author2_role author
author
author
author
author
author_facet Azar, Danielle
Moussa, Rebecca
Gerges, Firas
Salem, Christian
Akiki, Romario
Falou, Omar
author_role author
dc.creator.none.fl_str_mv Azar, Danielle
Moussa, Rebecca
Gerges, Firas
Salem, Christian
Akiki, Romario
Falou, Omar
dc.date.none.fl_str_mv 2017-03-14T12:54:31Z
2017-03-14T12:54:31Z
2017-03-14
dc.identifier.none.fl_str_mv http://hdl.handle.net/10725/5368
http://dx.doi.org/10.1109/MECBME.2016.7745423
Moussa, R., Gerges, F., Salem, C., Akiki, R., Falou, O., & Azar, D. (2016, October). Computer-aided detection of Melanoma using geometric features. In Biomedical Engineering (MECBME), 2016 3rd Middle East Conference on (pp. 125-128). IEEE.
http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php
http://ieeexplore.ieee.org/abstract/document/7745423/
dc.language.none.fl_str_mv en
dc.publisher.none.fl_str_mv IEEE
dc.rights.*.fl_str_mv info:eu-repo/semantics/openAccess
dc.title.none.fl_str_mv Computer-aided detection of Melanoma using geometric features
dc.type.none.fl_str_mv Conference Paper / Proceeding
info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/conferenceObject
description Melanoma is one type of skin cancer that usually develops from prolonged exposure to UV light. The latter triggers mutations that lead skin cells to multiply rapidly and form malignant tumors. If not cured, Melanoma can result in one's death. Hence, an early detection of this deadly cancer is important to prevent it. Certain lesion characteristics such as Asymmetry, Border, Color and Diameter segmentation (ABCD rule), can indicate the presence of Melanoma. In this work, we investigate the use of geometric features to differentiate between a benign lesion and a malignant one. The k-Nearest Neighbors (k-NN) machine learning algorithm is used to classify 15 lesions based on their ABD features. An accuracy of 89% was obtained on the testing set. The results indicate that this technique may be used to detect Melanoma skin cancer.
eu_rights_str_mv openAccess
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identifier_str_mv Moussa, R., Gerges, F., Salem, C., Akiki, R., Falou, O., & Azar, D. (2016, October). Computer-aided detection of Melanoma using geometric features. In Biomedical Engineering (MECBME), 2016 3rd Middle East Conference on (pp. 125-128). IEEE.
language_invalid_str_mv en
network_acronym_str LAURepo
network_name_str Lebanese American University repository
oai_identifier_str oai:laur.lau.edu.lb:10725/5368
publishDate 2017
publisher.none.fl_str_mv IEEE
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spelling Computer-aided detection of Melanoma using geometric featuresAzar, DanielleMoussa, RebeccaGerges, FirasSalem, ChristianAkiki, RomarioFalou, OmarMelanoma is one type of skin cancer that usually develops from prolonged exposure to UV light. The latter triggers mutations that lead skin cells to multiply rapidly and form malignant tumors. If not cured, Melanoma can result in one's death. Hence, an early detection of this deadly cancer is important to prevent it. Certain lesion characteristics such as Asymmetry, Border, Color and Diameter segmentation (ABCD rule), can indicate the presence of Melanoma. In this work, we investigate the use of geometric features to differentiate between a benign lesion and a malignant one. The k-Nearest Neighbors (k-NN) machine learning algorithm is used to classify 15 lesions based on their ABD features. An accuracy of 89% was obtained on the testing set. The results indicate that this technique may be used to detect Melanoma skin cancer.N/AIEEE2017-03-14T12:54:31Z2017-03-14T12:54:31Z2017-03-14Conference Paper / Proceedinginfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://hdl.handle.net/10725/5368http://dx.doi.org/10.1109/MECBME.2016.7745423Moussa, R., Gerges, F., Salem, C., Akiki, R., Falou, O., & Azar, D. (2016, October). Computer-aided detection of Melanoma using geometric features. In Biomedical Engineering (MECBME), 2016 3rd Middle East Conference on (pp. 125-128). IEEE.http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.phphttp://ieeexplore.ieee.org/abstract/document/7745423/eninfo:eu-repo/semantics/openAccessoai:laur.lau.edu.lb:10725/53682021-03-19T10:03:24Z
spellingShingle Computer-aided detection of Melanoma using geometric features
Azar, Danielle
status_str publishedVersion
title Computer-aided detection of Melanoma using geometric features
title_full Computer-aided detection of Melanoma using geometric features
title_fullStr Computer-aided detection of Melanoma using geometric features
title_full_unstemmed Computer-aided detection of Melanoma using geometric features
title_short Computer-aided detection of Melanoma using geometric features
title_sort Computer-aided detection of Melanoma using geometric features
url http://hdl.handle.net/10725/5368
http://dx.doi.org/10.1109/MECBME.2016.7745423
http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php
http://ieeexplore.ieee.org/abstract/document/7745423/