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A novel approach toward skin cancer classification through fused deep features and neutrosophic environment
Variations in the size and texture of melanoma make the classification procedure more complex in a computer-aided diagnostic (CAD) system. The research proposes an innovative hybrid deep learning-based layer-fusion and neutrosophic-set technique for identifying skin lesions. The off-the-shelf networ...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10150637/ https://www.ncbi.nlm.nih.gov/pubmed/37139387 http://dx.doi.org/10.3389/fpubh.2023.1123581 |
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author | Abdelhafeez, Ahmed Mohamed, Hoda K. Maher, Ali Khalil, Nariman A. |
author_facet | Abdelhafeez, Ahmed Mohamed, Hoda K. Maher, Ali Khalil, Nariman A. |
author_sort | Abdelhafeez, Ahmed |
collection | PubMed |
description | Variations in the size and texture of melanoma make the classification procedure more complex in a computer-aided diagnostic (CAD) system. The research proposes an innovative hybrid deep learning-based layer-fusion and neutrosophic-set technique for identifying skin lesions. The off-the-shelf networks are examined to categorize eight types of skin lesions using transfer learning on International Skin Imaging Collaboration (ISIC) 2019 skin lesion datasets. The top two networks, which are GoogleNet and DarkNet, achieved an accuracy of 77.41 and 82.42%, respectively. The proposed method works in two successive stages: first, boosting the classification accuracy of the trained networks individually. A suggested feature fusion methodology is applied to enrich the extracted features’ descriptive power, which promotes the accuracy to 79.2 and 84.5%, respectively. The second stage explores how to combine these networks for further improvement. The error-correcting output codes (ECOC) paradigm is utilized for constructing a set of well-trained true and false support vector machine (SVM) classifiers via fused DarkNet and GoogleNet feature maps, respectively. The ECOC’s coding matrices are designed to train each true classifier and its opponent in a one-versus-other fashion. Consequently, contradictions between true and false classifiers in terms of their classification scores create an ambiguity zone quantified by the indeterminacy set. Recent neutrosophic techniques resolve this ambiguity to tilt the balance toward the correct skin cancer class. As a result, the classification score is increased to 85.74%, outperforming the recent proposals by an obvious step. The trained models alongside the implementation of the proposed single-valued neutrosophic sets (SVNSs) will be publicly available for aiding relevant research fields. |
format | Online Article Text |
id | pubmed-10150637 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101506372023-05-02 A novel approach toward skin cancer classification through fused deep features and neutrosophic environment Abdelhafeez, Ahmed Mohamed, Hoda K. Maher, Ali Khalil, Nariman A. Front Public Health Public Health Variations in the size and texture of melanoma make the classification procedure more complex in a computer-aided diagnostic (CAD) system. The research proposes an innovative hybrid deep learning-based layer-fusion and neutrosophic-set technique for identifying skin lesions. The off-the-shelf networks are examined to categorize eight types of skin lesions using transfer learning on International Skin Imaging Collaboration (ISIC) 2019 skin lesion datasets. The top two networks, which are GoogleNet and DarkNet, achieved an accuracy of 77.41 and 82.42%, respectively. The proposed method works in two successive stages: first, boosting the classification accuracy of the trained networks individually. A suggested feature fusion methodology is applied to enrich the extracted features’ descriptive power, which promotes the accuracy to 79.2 and 84.5%, respectively. The second stage explores how to combine these networks for further improvement. The error-correcting output codes (ECOC) paradigm is utilized for constructing a set of well-trained true and false support vector machine (SVM) classifiers via fused DarkNet and GoogleNet feature maps, respectively. The ECOC’s coding matrices are designed to train each true classifier and its opponent in a one-versus-other fashion. Consequently, contradictions between true and false classifiers in terms of their classification scores create an ambiguity zone quantified by the indeterminacy set. Recent neutrosophic techniques resolve this ambiguity to tilt the balance toward the correct skin cancer class. As a result, the classification score is increased to 85.74%, outperforming the recent proposals by an obvious step. The trained models alongside the implementation of the proposed single-valued neutrosophic sets (SVNSs) will be publicly available for aiding relevant research fields. Frontiers Media S.A. 2023-04-17 /pmc/articles/PMC10150637/ /pubmed/37139387 http://dx.doi.org/10.3389/fpubh.2023.1123581 Text en Copyright © 2023 Abdelhafeez, Mohamed, Maher and Khalil. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Public Health Abdelhafeez, Ahmed Mohamed, Hoda K. Maher, Ali Khalil, Nariman A. A novel approach toward skin cancer classification through fused deep features and neutrosophic environment |
title | A novel approach toward skin cancer classification through fused deep features and neutrosophic environment |
title_full | A novel approach toward skin cancer classification through fused deep features and neutrosophic environment |
title_fullStr | A novel approach toward skin cancer classification through fused deep features and neutrosophic environment |
title_full_unstemmed | A novel approach toward skin cancer classification through fused deep features and neutrosophic environment |
title_short | A novel approach toward skin cancer classification through fused deep features and neutrosophic environment |
title_sort | novel approach toward skin cancer classification through fused deep features and neutrosophic environment |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10150637/ https://www.ncbi.nlm.nih.gov/pubmed/37139387 http://dx.doi.org/10.3389/fpubh.2023.1123581 |
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