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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...

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Autores principales: Thapar, Puneet, Rakhra, Manik, Cazzato, Gerardo, Hossain, Md Shamim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
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.
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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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