Biomedical spectral imaging is a non-invasive, non-destructive method, and has an important role in melanoma detection and all skin lesions monitoring during their various stages. In addition to spatial information, it contains spectral information that describes structure such as melanin content, and melanoma thickness, which, very well improve the sensitivity and specificity of melanoma detection. This article aims to describe the design of a multispectral imaging system that utilizes Artificial Neural Networks and Genetic Algorithm (Artificial Intelligence) for spectral images classification, in order to reduce the processing time of spectral images, memory and cost of the system. All system (Hardware and Software) works as an automatic detection system for malignant melanoma, which identifies malignant melanoma and common (benign) nevi by using wavelength scanning method with; CCD camera, filters wheel, and only eight optical filters range from 430nm to 620nm. 47 study cases were imaged. Good results were obtained: the sensitivity 91.67% and the specificity 91.43%.
| Published in |
American Journal of Biomedical and Life Sciences (Volume 3, Issue 2-3)
This article belongs to the Special Issue Spectral Imaging for Medical Diagnosis “Modern Tool for Molecular Imaging” |
| DOI | 10.11648/j.ajbls.s.2015030203.16 |
| Page(s) | 29-33 |
| 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 |
Melanoma Detection, Spectral Imaging, Artificial Intelligence, Artificial Neural Networks, Genetic Algorithm, Images Classification
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APA Style
Moataz Aboras, Hani Amasha, Issa Ibraheem. (2015). Early detection of melanoma using multispectral imaging and artificial intelligence techniques. American Journal of Biomedical and Life Sciences, 3(2-3), 29-33. https://doi.org/10.11648/j.ajbls.s.2015030203.16
ACS Style
Moataz Aboras; Hani Amasha; Issa Ibraheem. Early detection of melanoma using multispectral imaging and artificial intelligence techniques. Am. J. Biomed. Life Sci. 2015, 3(2-3), 29-33. doi: 10.11648/j.ajbls.s.2015030203.16
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Download@article{10.11648/j.ajbls.s.2015030203.16,
author = {Moataz Aboras and Hani Amasha and Issa Ibraheem},
title = {Early detection of melanoma using multispectral imaging and artificial intelligence techniques},
journal = {American Journal of Biomedical and Life Sciences},
volume = {3},
number = {2-3},
pages = {29-33},
doi = {10.11648/j.ajbls.s.2015030203.16},
url = {https://doi.org/10.11648/j.ajbls.s.2015030203.16},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajbls.s.2015030203.16},
abstract = {Biomedical spectral imaging is a non-invasive, non-destructive method, and has an important role in melanoma detection and all skin lesions monitoring during their various stages. In addition to spatial information, it contains spectral information that describes structure such as melanin content, and melanoma thickness, which, very well improve the sensitivity and specificity of melanoma detection. This article aims to describe the design of a multispectral imaging system that utilizes Artificial Neural Networks and Genetic Algorithm (Artificial Intelligence) for spectral images classification, in order to reduce the processing time of spectral images, memory and cost of the system. All system (Hardware and Software) works as an automatic detection system for malignant melanoma, which identifies malignant melanoma and common (benign) nevi by using wavelength scanning method with; CCD camera, filters wheel, and only eight optical filters range from 430nm to 620nm. 47 study cases were imaged. Good results were obtained: the sensitivity 91.67% and the specificity 91.43%.},
year = {2015}
}
TY - JOUR T1 - Early detection of melanoma using multispectral imaging and artificial intelligence techniques AU - Moataz Aboras AU - Hani Amasha AU - Issa Ibraheem Y1 - 2015/08/07 PY - 2015 N1 - https://doi.org/10.11648/j.ajbls.s.2015030203.16 DO - 10.11648/j.ajbls.s.2015030203.16 T2 - American Journal of Biomedical and Life Sciences JF - American Journal of Biomedical and Life Sciences JO - American Journal of Biomedical and Life Sciences SP - 29 EP - 33 PB - Science Publishing Group SN - 2330-880X UR - https://doi.org/10.11648/j.ajbls.s.2015030203.16 AB - Biomedical spectral imaging is a non-invasive, non-destructive method, and has an important role in melanoma detection and all skin lesions monitoring during their various stages. In addition to spatial information, it contains spectral information that describes structure such as melanin content, and melanoma thickness, which, very well improve the sensitivity and specificity of melanoma detection. This article aims to describe the design of a multispectral imaging system that utilizes Artificial Neural Networks and Genetic Algorithm (Artificial Intelligence) for spectral images classification, in order to reduce the processing time of spectral images, memory and cost of the system. All system (Hardware and Software) works as an automatic detection system for malignant melanoma, which identifies malignant melanoma and common (benign) nevi by using wavelength scanning method with; CCD camera, filters wheel, and only eight optical filters range from 430nm to 620nm. 47 study cases were imaged. Good results were obtained: the sensitivity 91.67% and the specificity 91.43%. VL - 3 IS - 2-3 ER -
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