Validation Study of Supervised and Unsupervised Calcification-Algorithms Used to Detection of Melanoma

Issa Ibraheem

doi:doi:10.11648/j.ijbecs.20150101.11

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Validation Study of Supervised and Unsupervised Calcification-Algorithms Used to Detection of Melanoma

Issa Ibraheem

Published in International Journal of Biomedical Engineering and Clinical Science (Volume 1, Issue 1)

Received: 26 July 2015 Accepted: 12 August 2015 Published: 13 August 2015

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Abstract

Melanoma is a leading fatal illness responsible for 80% of deaths from skin cancer. It originates in the pigment-producing melanocytes in the basal layer of the epidermis. Melanocytes produce the melanin, (the dark pigment), which is responsible for the color of skin. As all cancers, melanoma is caused by damage to the DNA of the cells, which causes the cell to grow out of control, leading to a tumor, which is much more dangerous, if it cannot be found or detected early. Only biopsy can determine exact malformation diagnose, though it can rise metastasizing. When a melanoma is suspected, the usual standard procedure is to perform a biopsy and to subsequently analyze the suspicious tissue under the microscope. In this Paper, we provide a new approach using methods known as "Imaging Spectroscopy" or "Spectral Imaging" for early detection of melanoma. Spectral imaging can fill this gap of the classical imaging, which carries little spectral information while spectroscopy is severely limited in terms of measuring (potentially) inhomogeneous samples. Three different classifiers were applied, Maximum Likelihood ML and Spectral Angle Mapper SAM and K-Means. SAM rests on the spectral "angular distances" and the conventional classifier ML rests on the spectral distance concept. SAM and ML are two methods of the supported classification routines and K-Means is the known unsupported classification (clustering) algorithm

Published in	International Journal of Biomedical Engineering and Clinical Science (Volume 1, Issue 1)
DOI	10.11648/j.ijbecs.20150101.11
Page(s)	1-9
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Melanoma, Spectral Imaging, Spectroscopy, Supported Classification, Unsupported Classification, Cancer Detection

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Issa Ibraheem. (2015). Validation Study of Supervised and Unsupervised Calcification-Algorithms Used to Detection of Melanoma. International Journal of Biomedical Engineering and Clinical Science, 1(1), 1-9. https://doi.org/10.11648/j.ijbecs.20150101.11

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Issa Ibraheem. Validation Study of Supervised and Unsupervised Calcification-Algorithms Used to Detection of Melanoma. Int. J. Biomed. Eng. Clin. Sci. 2015, 1(1), 1-9. doi: 10.11648/j.ijbecs.20150101.11

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AMA Style

Issa Ibraheem. Validation Study of Supervised and Unsupervised Calcification-Algorithms Used to Detection of Melanoma. Int J Biomed Eng Clin Sci. 2015;1(1):1-9. doi: 10.11648/j.ijbecs.20150101.11

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@article{10.11648/j.ijbecs.20150101.11,
  author = {Issa Ibraheem},
  title = {Validation Study of Supervised and Unsupervised Calcification-Algorithms Used to Detection of Melanoma},
  journal = {International Journal of Biomedical Engineering and Clinical Science},
  volume = {1},
  number = {1},
  pages = {1-9},
  doi = {10.11648/j.ijbecs.20150101.11},
  url = {https://doi.org/10.11648/j.ijbecs.20150101.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijbecs.20150101.11},
  abstract = {Melanoma is a leading fatal illness responsible for 80% of deaths from skin cancer. It originates in the pigment-producing melanocytes in the basal layer of the epidermis. Melanocytes produce the melanin, (the dark pigment), which is responsible for the color of skin. As all cancers, melanoma is caused by damage to the DNA of the cells, which causes the cell to grow out of control, leading to a tumor, which is much more dangerous, if it cannot be found or detected early. Only biopsy can determine exact malformation diagnose, though it can rise metastasizing. When a melanoma is suspected, the usual standard procedure is to perform a biopsy and to subsequently analyze the suspicious tissue under the microscope. In this Paper, we provide a new approach using methods known as "Imaging Spectroscopy" or "Spectral Imaging" for early detection of melanoma. Spectral imaging can fill this gap of the classical imaging, which carries little spectral information while spectroscopy is severely limited in terms of measuring (potentially) inhomogeneous samples. Three different classifiers were applied, Maximum Likelihood ML and Spectral Angle Mapper SAM and K-Means. SAM rests on the spectral "angular distances" and the conventional classifier ML rests on the spectral distance concept. SAM and ML are two methods of the supported classification routines and K-Means is the known unsupported classification (clustering) algorithm},
 year = {2015}
}

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TY  - JOUR
T1  - Validation Study of Supervised and Unsupervised Calcification-Algorithms Used to Detection of Melanoma
AU  - Issa Ibraheem
Y1  - 2015/08/13
PY  - 2015
N1  - https://doi.org/10.11648/j.ijbecs.20150101.11
DO  - 10.11648/j.ijbecs.20150101.11
T2  - International Journal of Biomedical Engineering and Clinical Science
JF  - International Journal of Biomedical Engineering and Clinical Science
JO  - International Journal of Biomedical Engineering and Clinical Science
SP  - 1
EP  - 9
PB  - Science Publishing Group
SN  - 2472-1301
UR  - https://doi.org/10.11648/j.ijbecs.20150101.11
AB  - Melanoma is a leading fatal illness responsible for 80% of deaths from skin cancer. It originates in the pigment-producing melanocytes in the basal layer of the epidermis. Melanocytes produce the melanin, (the dark pigment), which is responsible for the color of skin. As all cancers, melanoma is caused by damage to the DNA of the cells, which causes the cell to grow out of control, leading to a tumor, which is much more dangerous, if it cannot be found or detected early. Only biopsy can determine exact malformation diagnose, though it can rise metastasizing. When a melanoma is suspected, the usual standard procedure is to perform a biopsy and to subsequently analyze the suspicious tissue under the microscope. In this Paper, we provide a new approach using methods known as "Imaging Spectroscopy" or "Spectral Imaging" for early detection of melanoma. Spectral imaging can fill this gap of the classical imaging, which carries little spectral information while spectroscopy is severely limited in terms of measuring (potentially) inhomogeneous samples. Three different classifiers were applied, Maximum Likelihood ML and Spectral Angle Mapper SAM and K-Means. SAM rests on the spectral "angular distances" and the conventional classifier ML rests on the spectral distance concept. SAM and ML are two methods of the supported classification routines and K-Means is the known unsupported classification (clustering) algorithm
VL  - 1
IS  - 1
ER  -

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Issa Ibraheem

Biomedical Engineering, Al-Andalus Private University for medical Sciences, Tartus, Syria

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Issa Ibraheem. (2015). Validation Study of Supervised and Unsupervised Calcification-Algorithms Used to Detection of Melanoma. International Journal of Biomedical Engineering and Clinical Science, 1(1), 1-9. https://doi.org/10.11648/j.ijbecs.20150101.11

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ACS Style

Issa Ibraheem. Validation Study of Supervised and Unsupervised Calcification-Algorithms Used to Detection of Melanoma. Int. J. Biomed. Eng. Clin. Sci. 2015, 1(1), 1-9. doi: 10.11648/j.ijbecs.20150101.11

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AMA Style

Issa Ibraheem. Validation Study of Supervised and Unsupervised Calcification-Algorithms Used to Detection of Melanoma. Int J Biomed Eng Clin Sci. 2015;1(1):1-9. doi: 10.11648/j.ijbecs.20150101.11

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@article{10.11648/j.ijbecs.20150101.11,
  author = {Issa Ibraheem},
  title = {Validation Study of Supervised and Unsupervised Calcification-Algorithms Used to Detection of Melanoma},
  journal = {International Journal of Biomedical Engineering and Clinical Science},
  volume = {1},
  number = {1},
  pages = {1-9},
  doi = {10.11648/j.ijbecs.20150101.11},
  url = {https://doi.org/10.11648/j.ijbecs.20150101.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijbecs.20150101.11},
  abstract = {Melanoma is a leading fatal illness responsible for 80% of deaths from skin cancer. It originates in the pigment-producing melanocytes in the basal layer of the epidermis. Melanocytes produce the melanin, (the dark pigment), which is responsible for the color of skin. As all cancers, melanoma is caused by damage to the DNA of the cells, which causes the cell to grow out of control, leading to a tumor, which is much more dangerous, if it cannot be found or detected early. Only biopsy can determine exact malformation diagnose, though it can rise metastasizing. When a melanoma is suspected, the usual standard procedure is to perform a biopsy and to subsequently analyze the suspicious tissue under the microscope. In this Paper, we provide a new approach using methods known as "Imaging Spectroscopy" or "Spectral Imaging" for early detection of melanoma. Spectral imaging can fill this gap of the classical imaging, which carries little spectral information while spectroscopy is severely limited in terms of measuring (potentially) inhomogeneous samples. Three different classifiers were applied, Maximum Likelihood ML and Spectral Angle Mapper SAM and K-Means. SAM rests on the spectral "angular distances" and the conventional classifier ML rests on the spectral distance concept. SAM and ML are two methods of the supported classification routines and K-Means is the known unsupported classification (clustering) algorithm},
 year = {2015}
}

Copy | Download

TY  - JOUR
T1  - Validation Study of Supervised and Unsupervised Calcification-Algorithms Used to Detection of Melanoma
AU  - Issa Ibraheem
Y1  - 2015/08/13
PY  - 2015
N1  - https://doi.org/10.11648/j.ijbecs.20150101.11
DO  - 10.11648/j.ijbecs.20150101.11
T2  - International Journal of Biomedical Engineering and Clinical Science
JF  - International Journal of Biomedical Engineering and Clinical Science
JO  - International Journal of Biomedical Engineering and Clinical Science
SP  - 1
EP  - 9
PB  - Science Publishing Group
SN  - 2472-1301
UR  - https://doi.org/10.11648/j.ijbecs.20150101.11
AB  - Melanoma is a leading fatal illness responsible for 80% of deaths from skin cancer. It originates in the pigment-producing melanocytes in the basal layer of the epidermis. Melanocytes produce the melanin, (the dark pigment), which is responsible for the color of skin. As all cancers, melanoma is caused by damage to the DNA of the cells, which causes the cell to grow out of control, leading to a tumor, which is much more dangerous, if it cannot be found or detected early. Only biopsy can determine exact malformation diagnose, though it can rise metastasizing. When a melanoma is suspected, the usual standard procedure is to perform a biopsy and to subsequently analyze the suspicious tissue under the microscope. In this Paper, we provide a new approach using methods known as "Imaging Spectroscopy" or "Spectral Imaging" for early detection of melanoma. Spectral imaging can fill this gap of the classical imaging, which carries little spectral information while spectroscopy is severely limited in terms of measuring (potentially) inhomogeneous samples. Three different classifiers were applied, Maximum Likelihood ML and Spectral Angle Mapper SAM and K-Means. SAM rests on the spectral "angular distances" and the conventional classifier ML rests on the spectral distance concept. SAM and ML are two methods of the supported classification routines and K-Means is the known unsupported classification (clustering) algorithm
VL  - 1
IS  - 1
ER  -

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