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A Hybrid Stacked Restricted Boltzmann Machine with Sobel Directional Patterns for Melanoma Prediction in Colored Skin Images

Melanoma, a kind of skin cancer that is very risky, is distinguished by uncontrolled cell multiplication. Melanoma detection is of the utmost significance in clinical practice because of the atypical border structure and the numerous types of tissue it can involve. The identification of melanoma is...

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Autores principales: Alphonse, A. Sherly, Benifa, J. V. Bibal, Muaad, Abdullah Y., Chola, Channabasava, Heyat, Md Belal Bin, Murshed, Belal Abdullah Hezam, Abdel Samee, Nagwan, Alabdulhafith, Maali, Al-antari, Mugahed A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10047753/
https://www.ncbi.nlm.nih.gov/pubmed/36980412
http://dx.doi.org/10.3390/diagnostics13061104
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author Alphonse, A. Sherly
Benifa, J. V. Bibal
Muaad, Abdullah Y.
Chola, Channabasava
Heyat, Md Belal Bin
Murshed, Belal Abdullah Hezam
Abdel Samee, Nagwan
Alabdulhafith, Maali
Al-antari, Mugahed A.
author_facet Alphonse, A. Sherly
Benifa, J. V. Bibal
Muaad, Abdullah Y.
Chola, Channabasava
Heyat, Md Belal Bin
Murshed, Belal Abdullah Hezam
Abdel Samee, Nagwan
Alabdulhafith, Maali
Al-antari, Mugahed A.
author_sort Alphonse, A. Sherly
collection PubMed
description Melanoma, a kind of skin cancer that is very risky, is distinguished by uncontrolled cell multiplication. Melanoma detection is of the utmost significance in clinical practice because of the atypical border structure and the numerous types of tissue it can involve. The identification of melanoma is still a challenging process for color images, despite the fact that numerous approaches have been proposed in the research that has been done. In this research, we present a comprehensive system for the efficient and precise classification of skin lesions. The framework includes preprocessing, segmentation, feature extraction, and classification modules. Preprocessing with DullRazor eliminates skin-imaging hair artifacts. Next, Fully Connected Neural Network (FCNN) semantic segmentation extracts precise and obvious Regions of Interest (ROIs). We then extract relevant skin image features from ROIs using an enhanced Sobel Directional Pattern (SDP). For skin image analysis, Sobel Directional Pattern outperforms ABCD. Finally, a stacked Restricted Boltzmann Machine (RBM) classifies skin ROIs. Stacked RBMs accurately classify skin melanoma. The experiments have been conducted on five datasets: Pedro Hispano Hospital (PH2), International Skin Imaging Collaboration (ISIC 2016), ISIC 2017, Dermnet, and DermIS, and achieved an accuracy of 99.8%, 96.5%, 95.5%, 87.9%, and 97.6%, respectively. The results show that a stack of Restricted Boltzmann Machines is superior for categorizing skin cancer types using the proposed innovative SDP.
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spelling pubmed-100477532023-03-29 A Hybrid Stacked Restricted Boltzmann Machine with Sobel Directional Patterns for Melanoma Prediction in Colored Skin Images Alphonse, A. Sherly Benifa, J. V. Bibal Muaad, Abdullah Y. Chola, Channabasava Heyat, Md Belal Bin Murshed, Belal Abdullah Hezam Abdel Samee, Nagwan Alabdulhafith, Maali Al-antari, Mugahed A. Diagnostics (Basel) Article Melanoma, a kind of skin cancer that is very risky, is distinguished by uncontrolled cell multiplication. Melanoma detection is of the utmost significance in clinical practice because of the atypical border structure and the numerous types of tissue it can involve. The identification of melanoma is still a challenging process for color images, despite the fact that numerous approaches have been proposed in the research that has been done. In this research, we present a comprehensive system for the efficient and precise classification of skin lesions. The framework includes preprocessing, segmentation, feature extraction, and classification modules. Preprocessing with DullRazor eliminates skin-imaging hair artifacts. Next, Fully Connected Neural Network (FCNN) semantic segmentation extracts precise and obvious Regions of Interest (ROIs). We then extract relevant skin image features from ROIs using an enhanced Sobel Directional Pattern (SDP). For skin image analysis, Sobel Directional Pattern outperforms ABCD. Finally, a stacked Restricted Boltzmann Machine (RBM) classifies skin ROIs. Stacked RBMs accurately classify skin melanoma. The experiments have been conducted on five datasets: Pedro Hispano Hospital (PH2), International Skin Imaging Collaboration (ISIC 2016), ISIC 2017, Dermnet, and DermIS, and achieved an accuracy of 99.8%, 96.5%, 95.5%, 87.9%, and 97.6%, respectively. The results show that a stack of Restricted Boltzmann Machines is superior for categorizing skin cancer types using the proposed innovative SDP. MDPI 2023-03-14 /pmc/articles/PMC10047753/ /pubmed/36980412 http://dx.doi.org/10.3390/diagnostics13061104 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Alphonse, A. Sherly
Benifa, J. V. Bibal
Muaad, Abdullah Y.
Chola, Channabasava
Heyat, Md Belal Bin
Murshed, Belal Abdullah Hezam
Abdel Samee, Nagwan
Alabdulhafith, Maali
Al-antari, Mugahed A.
A Hybrid Stacked Restricted Boltzmann Machine with Sobel Directional Patterns for Melanoma Prediction in Colored Skin Images
title A Hybrid Stacked Restricted Boltzmann Machine with Sobel Directional Patterns for Melanoma Prediction in Colored Skin Images
title_full A Hybrid Stacked Restricted Boltzmann Machine with Sobel Directional Patterns for Melanoma Prediction in Colored Skin Images
title_fullStr A Hybrid Stacked Restricted Boltzmann Machine with Sobel Directional Patterns for Melanoma Prediction in Colored Skin Images
title_full_unstemmed A Hybrid Stacked Restricted Boltzmann Machine with Sobel Directional Patterns for Melanoma Prediction in Colored Skin Images
title_short A Hybrid Stacked Restricted Boltzmann Machine with Sobel Directional Patterns for Melanoma Prediction in Colored Skin Images
title_sort hybrid stacked restricted boltzmann machine with sobel directional patterns for melanoma prediction in colored skin images
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10047753/
https://www.ncbi.nlm.nih.gov/pubmed/36980412
http://dx.doi.org/10.3390/diagnostics13061104
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