Skin Cancer Detection Based on Extreme Learning Machine and a Developed Version of Thermal Exchange Optimization

Melanoma is defined as a disease that has been incurable in advanced stages, which shows the vital importance of timely diagnosis and treatment. To diagnose this type of cancer early, various methods and equipment have been used, almost all of which required a visit to the doctor and were not availa...

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Autores principales:	Shi Wang, Melika Hamian
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/1444e7a4541642ed8580e10a64de3a32
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spelling	oai:doaj.org-article:1444e7a4541642ed8580e10a64de3a322021-11-15T01:19:43ZSkin Cancer Detection Based on Extreme Learning Machine and a Developed Version of Thermal Exchange Optimization1687-527310.1155/2021/9528664https://doaj.org/article/1444e7a4541642ed8580e10a64de3a322021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/9528664https://doaj.org/toc/1687-5273Melanoma is defined as a disease that has been incurable in advanced stages, which shows the vital importance of timely diagnosis and treatment. To diagnose this type of cancer early, various methods and equipment have been used, almost all of which required a visit to the doctor and were not available to the public. In this study, an automated and accurate process to differentiate between benign skin pigmented lesions and malignant melanoma is presented, so that it can be used by the general public, and it does not require special equipment and special conditions in imaging. In this study, after preprocessing of the input images, the region of interest is segmented based on the Otsu method. Then, a new feature extraction is implemented on the segmented image to mine the beneficial characteristics. The process is then finalized by using an optimized Deep Believe Network (DBN) for categorization into 2 classes of normal and melanoma cases. The optimization process in DBN has been performed by a developed version of the newly introduced Thermal Exchange Optimization (dTEO) algorithm to obtain higher efficacy in different terms. To show the method’s superiority, its performance is compared with 7 different techniques from the literature.Shi WangMelika HamianHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7Neurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENComputational Intelligence and Neuroscience, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Shi Wang Melika Hamian Skin Cancer Detection Based on Extreme Learning Machine and a Developed Version of Thermal Exchange Optimization
description	Melanoma is defined as a disease that has been incurable in advanced stages, which shows the vital importance of timely diagnosis and treatment. To diagnose this type of cancer early, various methods and equipment have been used, almost all of which required a visit to the doctor and were not available to the public. In this study, an automated and accurate process to differentiate between benign skin pigmented lesions and malignant melanoma is presented, so that it can be used by the general public, and it does not require special equipment and special conditions in imaging. In this study, after preprocessing of the input images, the region of interest is segmented based on the Otsu method. Then, a new feature extraction is implemented on the segmented image to mine the beneficial characteristics. The process is then finalized by using an optimized Deep Believe Network (DBN) for categorization into 2 classes of normal and melanoma cases. The optimization process in DBN has been performed by a developed version of the newly introduced Thermal Exchange Optimization (dTEO) algorithm to obtain higher efficacy in different terms. To show the method’s superiority, its performance is compared with 7 different techniques from the literature.
format	article
author	Shi Wang Melika Hamian
author_facet	Shi Wang Melika Hamian
author_sort	Shi Wang
title	Skin Cancer Detection Based on Extreme Learning Machine and a Developed Version of Thermal Exchange Optimization
title_short	Skin Cancer Detection Based on Extreme Learning Machine and a Developed Version of Thermal Exchange Optimization
title_full	Skin Cancer Detection Based on Extreme Learning Machine and a Developed Version of Thermal Exchange Optimization
title_fullStr	Skin Cancer Detection Based on Extreme Learning Machine and a Developed Version of Thermal Exchange Optimization
title_full_unstemmed	Skin Cancer Detection Based on Extreme Learning Machine and a Developed Version of Thermal Exchange Optimization
title_sort	skin cancer detection based on extreme learning machine and a developed version of thermal exchange optimization
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/1444e7a4541642ed8580e10a64de3a32
work_keys_str_mv	AT shiwang skincancerdetectionbasedonextremelearningmachineandadevelopedversionofthermalexchangeoptimization AT melikahamian skincancerdetectionbasedonextremelearningmachineandadevelopedversionofthermalexchangeoptimization
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Skin Cancer Detection Based on Extreme Learning Machine and a Developed Version of Thermal Exchange Optimization

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