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A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification
Skin cancer is one of the most common diseases that can be initially detected by visual observation and further with the help of dermoscopic analysis and other tests. As at an initial stage, visual observation gives the opportunity of utilizing artificial intelligence to intercept the different skin...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9038388/ https://www.ncbi.nlm.nih.gov/pubmed/35480147 http://dx.doi.org/10.1155/2022/1709842 |
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author | Thapar, Puneet Rakhra, Manik Cazzato, Gerardo Hossain, Md Shamim |
author_facet | Thapar, Puneet Rakhra, Manik Cazzato, Gerardo Hossain, Md Shamim |
author_sort | Thapar, Puneet |
collection | PubMed |
description | Skin cancer is one of the most common diseases that can be initially detected by visual observation and further with the help of dermoscopic analysis and other tests. As at an initial stage, visual observation gives the opportunity of utilizing artificial intelligence to intercept the different skin images, so several skin lesion classification methods using deep learning based on convolution neural network (CNN) and annotated skin photos exhibit improved results. In this respect, the paper presents a reliable approach for diagnosing skin cancer utilizing dermoscopy images in order to improve health care professionals' visual perception and diagnostic abilities to discriminate benign from malignant lesions. The swarm intelligence (SI) algorithms were used for skin lesion region of interest (RoI) segmentation from dermoscopy images, and the speeded-up robust features (SURF) was used for feature extraction of the RoI marked as the best segmentation result obtained using the Grasshopper Optimization Algorithm (GOA). The skin lesions are classified into two groups using CNN against three data sets, namely, ISIC-2017, ISIC-2018, and PH-2 data sets. The proposed segmentation and classification techniques' results are assessed in terms of classification accuracy, sensitivity, specificity, F-measure, precision, MCC, dice coefficient, and Jaccard index, with an average classification accuracy of 98.42 percent, precision of 97.73 percent, and MCC of 0.9704 percent. In every performance measure, our suggested strategy exceeds previous work. |
format | Online Article Text |
id | pubmed-9038388 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-90383882022-04-26 A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification Thapar, Puneet Rakhra, Manik Cazzato, Gerardo Hossain, Md Shamim J Healthc Eng Research Article Skin cancer is one of the most common diseases that can be initially detected by visual observation and further with the help of dermoscopic analysis and other tests. As at an initial stage, visual observation gives the opportunity of utilizing artificial intelligence to intercept the different skin images, so several skin lesion classification methods using deep learning based on convolution neural network (CNN) and annotated skin photos exhibit improved results. In this respect, the paper presents a reliable approach for diagnosing skin cancer utilizing dermoscopy images in order to improve health care professionals' visual perception and diagnostic abilities to discriminate benign from malignant lesions. The swarm intelligence (SI) algorithms were used for skin lesion region of interest (RoI) segmentation from dermoscopy images, and the speeded-up robust features (SURF) was used for feature extraction of the RoI marked as the best segmentation result obtained using the Grasshopper Optimization Algorithm (GOA). The skin lesions are classified into two groups using CNN against three data sets, namely, ISIC-2017, ISIC-2018, and PH-2 data sets. The proposed segmentation and classification techniques' results are assessed in terms of classification accuracy, sensitivity, specificity, F-measure, precision, MCC, dice coefficient, and Jaccard index, with an average classification accuracy of 98.42 percent, precision of 97.73 percent, and MCC of 0.9704 percent. In every performance measure, our suggested strategy exceeds previous work. Hindawi 2022-04-18 /pmc/articles/PMC9038388/ /pubmed/35480147 http://dx.doi.org/10.1155/2022/1709842 Text en Copyright © 2022 Puneet Thapar et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Thapar, Puneet Rakhra, Manik Cazzato, Gerardo Hossain, Md Shamim A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification |
title | A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification |
title_full | A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification |
title_fullStr | A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification |
title_full_unstemmed | A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification |
title_short | A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification |
title_sort | novel hybrid deep learning approach for skin lesion segmentation and classification |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9038388/ https://www.ncbi.nlm.nih.gov/pubmed/35480147 http://dx.doi.org/10.1155/2022/1709842 |
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