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An Improved Skin Lesion Boundary Estimation for Enhanced-Intensity Images Using Hybrid Metaheuristics
The demand for the accurate and timely identification of melanoma as a major skin cancer type is increasing daily. Due to the advent of modern tools and computer vision techniques, it has become easier to perform analysis. Skin cancer classification and segmentation techniques require clear lesions...
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/PMC10093272/ https://www.ncbi.nlm.nih.gov/pubmed/37046503 http://dx.doi.org/10.3390/diagnostics13071285 |
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author | Malik, Shairyar Akram, Tallha Awais, Muhammad Khan, Muhammad Attique Hadjouni, Myriam Elmannai, Hela Alasiry, Areej Marzougui, Mehrez Tariq, Usman |
author_facet | Malik, Shairyar Akram, Tallha Awais, Muhammad Khan, Muhammad Attique Hadjouni, Myriam Elmannai, Hela Alasiry, Areej Marzougui, Mehrez Tariq, Usman |
author_sort | Malik, Shairyar |
collection | PubMed |
description | The demand for the accurate and timely identification of melanoma as a major skin cancer type is increasing daily. Due to the advent of modern tools and computer vision techniques, it has become easier to perform analysis. Skin cancer classification and segmentation techniques require clear lesions segregated from the background for efficient results. Many studies resolve the matter partly. However, there exists plenty of room for new research in this field. Recently, many algorithms have been presented to preprocess skin lesions, aiding the segmentation algorithms to generate efficient outcomes. Nature-inspired algorithms and metaheuristics help to estimate the optimal parameter set in the search space. This research article proposes a hybrid metaheuristic preprocessor, BA-ABC, to improve the quality of images by enhancing their contrast and preserving the brightness. The statistical transformation function, which helps to improve the contrast, is based on a parameter set estimated through the proposed hybrid metaheuristic model for every image in the dataset. For experimentation purposes, we have utilised three publicly available datasets, ISIC-2016, 2017 and 2018. The efficacy of the presented model is validated through some state-of-the-art segmentation algorithms. The visual outcomes of the boundary estimation algorithms and performance matrix validate that the proposed model performs well. The proposed model improves the dice coefficient to 94.6% in the results. |
format | Online Article Text |
id | pubmed-10093272 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100932722023-04-13 An Improved Skin Lesion Boundary Estimation for Enhanced-Intensity Images Using Hybrid Metaheuristics Malik, Shairyar Akram, Tallha Awais, Muhammad Khan, Muhammad Attique Hadjouni, Myriam Elmannai, Hela Alasiry, Areej Marzougui, Mehrez Tariq, Usman Diagnostics (Basel) Article The demand for the accurate and timely identification of melanoma as a major skin cancer type is increasing daily. Due to the advent of modern tools and computer vision techniques, it has become easier to perform analysis. Skin cancer classification and segmentation techniques require clear lesions segregated from the background for efficient results. Many studies resolve the matter partly. However, there exists plenty of room for new research in this field. Recently, many algorithms have been presented to preprocess skin lesions, aiding the segmentation algorithms to generate efficient outcomes. Nature-inspired algorithms and metaheuristics help to estimate the optimal parameter set in the search space. This research article proposes a hybrid metaheuristic preprocessor, BA-ABC, to improve the quality of images by enhancing their contrast and preserving the brightness. The statistical transformation function, which helps to improve the contrast, is based on a parameter set estimated through the proposed hybrid metaheuristic model for every image in the dataset. For experimentation purposes, we have utilised three publicly available datasets, ISIC-2016, 2017 and 2018. The efficacy of the presented model is validated through some state-of-the-art segmentation algorithms. The visual outcomes of the boundary estimation algorithms and performance matrix validate that the proposed model performs well. The proposed model improves the dice coefficient to 94.6% in the results. MDPI 2023-03-28 /pmc/articles/PMC10093272/ /pubmed/37046503 http://dx.doi.org/10.3390/diagnostics13071285 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 Malik, Shairyar Akram, Tallha Awais, Muhammad Khan, Muhammad Attique Hadjouni, Myriam Elmannai, Hela Alasiry, Areej Marzougui, Mehrez Tariq, Usman An Improved Skin Lesion Boundary Estimation for Enhanced-Intensity Images Using Hybrid Metaheuristics |
title | An Improved Skin Lesion Boundary Estimation for Enhanced-Intensity Images Using Hybrid Metaheuristics |
title_full | An Improved Skin Lesion Boundary Estimation for Enhanced-Intensity Images Using Hybrid Metaheuristics |
title_fullStr | An Improved Skin Lesion Boundary Estimation for Enhanced-Intensity Images Using Hybrid Metaheuristics |
title_full_unstemmed | An Improved Skin Lesion Boundary Estimation for Enhanced-Intensity Images Using Hybrid Metaheuristics |
title_short | An Improved Skin Lesion Boundary Estimation for Enhanced-Intensity Images Using Hybrid Metaheuristics |
title_sort | improved skin lesion boundary estimation for enhanced-intensity images using hybrid metaheuristics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10093272/ https://www.ncbi.nlm.nih.gov/pubmed/37046503 http://dx.doi.org/10.3390/diagnostics13071285 |
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