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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-10047753 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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