An image processing and genetic algorithm-based approach for the detection of melanoma in patients

Melanoma skin cancer is the most aggressive type of skin cancer. It is most commonly caused by excessive exposure to Ultraviolet radiation which triggers uncontrollable proliferation of melanocytes. Early detection makes melanoma relatively easily curable. Diagnosis is usually done using traditional...

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محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Tokajian, Sima (author)
مؤلفون آخرون: Azar, Danielle (author), Salem, Christian (author)
التنسيق: article
منشور في: 2018
الوصول للمادة أونلاين:http://hdl.handle.net/10725/8099
http://dx.doi.org/10.3412/ME17-01-0061
http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php
https://www.thieme-connect.com/products/ejournals/abstract/10.3412/ME17-01-0061
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author Tokajian, Sima
author2 Azar, Danielle
Salem, Christian
author2_role author
author
author_facet Tokajian, Sima
Azar, Danielle
Salem, Christian
author_role author
dc.creator.none.fl_str_mv Tokajian, Sima
Azar, Danielle
Salem, Christian
dc.date.none.fl_str_mv 2018-06-26T09:18:32Z
2018-06-26T09:18:32Z
2018
2018-06-26
dc.identifier.none.fl_str_mv 0026-1270
http://hdl.handle.net/10725/8099
http://dx.doi.org/10.3412/ME17-01-0061
Salem, C., Azar, D., & Tokajian, S. (2018). An Image Processing and Genetic Algorithm-based Approach for the Detection of Melanoma in Patients. Methods of information in medicine, 57(01), 74-80.
http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php
https://www.thieme-connect.com/products/ejournals/abstract/10.3412/ME17-01-0061
dc.language.none.fl_str_mv en
dc.relation.none.fl_str_mv Methods of Information in Medicine
dc.rights.*.fl_str_mv info:eu-repo/semantics/openAccess
dc.title.none.fl_str_mv An image processing and genetic algorithm-based approach for the detection of melanoma in patients
dc.type.none.fl_str_mv Article
info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
description Melanoma skin cancer is the most aggressive type of skin cancer. It is most commonly caused by excessive exposure to Ultraviolet radiation which triggers uncontrollable proliferation of melanocytes. Early detection makes melanoma relatively easily curable. Diagnosis is usually done using traditional methods such as dermoscopy which consists of a manual examination performed by the physician. However, these methods are not always well founded because they depend heavily on the physician’s experience. Hence, there is a great need for a new automated approach in order to make diagnosis more reliable. In this paper, we present a twophase technique to classify images of lesions into benign or malignant. The first phase consists of an image processing-based method that extracts the Asymmetry, Border Irregularity, Color Variation and Diameter of a given mole. The second phase classifies lesions using a Genetic Algorithm. Our technique shows a significant improvement over other well-known algorithms and proves to be more stable on both training and testing data.
eu_rights_str_mv openAccess
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Salem, C., Azar, D., & Tokajian, S. (2018). An Image Processing and Genetic Algorithm-based Approach for the Detection of Melanoma in Patients. Methods of information in medicine, 57(01), 74-80.
language_invalid_str_mv en
network_acronym_str LAURepo
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spelling An image processing and genetic algorithm-based approach for the detection of melanoma in patientsTokajian, SimaAzar, DanielleSalem, ChristianMelanoma skin cancer is the most aggressive type of skin cancer. It is most commonly caused by excessive exposure to Ultraviolet radiation which triggers uncontrollable proliferation of melanocytes. Early detection makes melanoma relatively easily curable. Diagnosis is usually done using traditional methods such as dermoscopy which consists of a manual examination performed by the physician. However, these methods are not always well founded because they depend heavily on the physician’s experience. Hence, there is a great need for a new automated approach in order to make diagnosis more reliable. In this paper, we present a twophase technique to classify images of lesions into benign or malignant. The first phase consists of an image processing-based method that extracts the Asymmetry, Border Irregularity, Color Variation and Diameter of a given mole. The second phase classifies lesions using a Genetic Algorithm. Our technique shows a significant improvement over other well-known algorithms and proves to be more stable on both training and testing data.PublishedN/A2018-06-26T09:18:32Z2018-06-26T09:18:32Z20182018-06-26Articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article0026-1270http://hdl.handle.net/10725/8099http://dx.doi.org/10.3412/ME17-01-0061Salem, C., Azar, D., & Tokajian, S. (2018). An Image Processing and Genetic Algorithm-based Approach for the Detection of Melanoma in Patients. Methods of information in medicine, 57(01), 74-80.http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.phphttps://www.thieme-connect.com/products/ejournals/abstract/10.3412/ME17-01-0061enMethods of Information in Medicineinfo:eu-repo/semantics/openAccessoai:laur.lau.edu.lb:10725/80992021-03-19T10:43:08Z
spellingShingle An image processing and genetic algorithm-based approach for the detection of melanoma in patients
Tokajian, Sima
status_str publishedVersion
title An image processing and genetic algorithm-based approach for the detection of melanoma in patients
title_full An image processing and genetic algorithm-based approach for the detection of melanoma in patients
title_fullStr An image processing and genetic algorithm-based approach for the detection of melanoma in patients
title_full_unstemmed An image processing and genetic algorithm-based approach for the detection of melanoma in patients
title_short An image processing and genetic algorithm-based approach for the detection of melanoma in patients
title_sort An image processing and genetic algorithm-based approach for the detection of melanoma in patients
url http://hdl.handle.net/10725/8099
http://dx.doi.org/10.3412/ME17-01-0061
http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php
https://www.thieme-connect.com/products/ejournals/abstract/10.3412/ME17-01-0061