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Melanoma Classification Using a Novel Deep Convolutional Neural Network with Dermoscopic Images

Automatic melanoma detection from dermoscopic skin samples is a very challenging task. However, using a deep learning approach as a machine vision tool can overcome some challenges. This research proposes an automated melanoma classifier based on a deep convolutional neural network (DCNN) to accurat...

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Autores principales: Kaur, Ranpreet, GholamHosseini, Hamid, Sinha, Roopak, Lindén, Maria
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8838143/
https://www.ncbi.nlm.nih.gov/pubmed/35161878
http://dx.doi.org/10.3390/s22031134
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author Kaur, Ranpreet
GholamHosseini, Hamid
Sinha, Roopak
Lindén, Maria
author_facet Kaur, Ranpreet
GholamHosseini, Hamid
Sinha, Roopak
Lindén, Maria
author_sort Kaur, Ranpreet
collection PubMed
description Automatic melanoma detection from dermoscopic skin samples is a very challenging task. However, using a deep learning approach as a machine vision tool can overcome some challenges. This research proposes an automated melanoma classifier based on a deep convolutional neural network (DCNN) to accurately classify malignant vs. benign melanoma. The structure of the DCNN is carefully designed by organizing many layers that are responsible for extracting low to high-level features of the skin images in a unique fashion. Other vital criteria in the design of DCNN are the selection of multiple filters and their sizes, employing proper deep learning layers, choosing the depth of the network, and optimizing hyperparameters. The primary objective is to propose a lightweight and less complex DCNN than other state-of-the-art methods to classify melanoma skin cancer with high efficiency. For this study, dermoscopic images containing different cancer samples were obtained from the International Skin Imaging Collaboration datastores (ISIC 2016, ISIC2017, and ISIC 2020). We evaluated the model based on accuracy, precision, recall, specificity, and F1-score. The proposed DCNN classifier achieved accuracies of 81.41%, 88.23%, and 90.42% on the ISIC 2016, 2017, and 2020 datasets, respectively, demonstrating high performance compared with the other state-of-the-art networks. Therefore, this proposed approach could provide a less complex and advanced framework for automating the melanoma diagnostic process and expediting the identification process to save a life.
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spelling pubmed-88381432022-02-13 Melanoma Classification Using a Novel Deep Convolutional Neural Network with Dermoscopic Images Kaur, Ranpreet GholamHosseini, Hamid Sinha, Roopak Lindén, Maria Sensors (Basel) Article Automatic melanoma detection from dermoscopic skin samples is a very challenging task. However, using a deep learning approach as a machine vision tool can overcome some challenges. This research proposes an automated melanoma classifier based on a deep convolutional neural network (DCNN) to accurately classify malignant vs. benign melanoma. The structure of the DCNN is carefully designed by organizing many layers that are responsible for extracting low to high-level features of the skin images in a unique fashion. Other vital criteria in the design of DCNN are the selection of multiple filters and their sizes, employing proper deep learning layers, choosing the depth of the network, and optimizing hyperparameters. The primary objective is to propose a lightweight and less complex DCNN than other state-of-the-art methods to classify melanoma skin cancer with high efficiency. For this study, dermoscopic images containing different cancer samples were obtained from the International Skin Imaging Collaboration datastores (ISIC 2016, ISIC2017, and ISIC 2020). We evaluated the model based on accuracy, precision, recall, specificity, and F1-score. The proposed DCNN classifier achieved accuracies of 81.41%, 88.23%, and 90.42% on the ISIC 2016, 2017, and 2020 datasets, respectively, demonstrating high performance compared with the other state-of-the-art networks. Therefore, this proposed approach could provide a less complex and advanced framework for automating the melanoma diagnostic process and expediting the identification process to save a life. MDPI 2022-02-02 /pmc/articles/PMC8838143/ /pubmed/35161878 http://dx.doi.org/10.3390/s22031134 Text en © 2022 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
Kaur, Ranpreet
GholamHosseini, Hamid
Sinha, Roopak
Lindén, Maria
Melanoma Classification Using a Novel Deep Convolutional Neural Network with Dermoscopic Images
title Melanoma Classification Using a Novel Deep Convolutional Neural Network with Dermoscopic Images
title_full Melanoma Classification Using a Novel Deep Convolutional Neural Network with Dermoscopic Images
title_fullStr Melanoma Classification Using a Novel Deep Convolutional Neural Network with Dermoscopic Images
title_full_unstemmed Melanoma Classification Using a Novel Deep Convolutional Neural Network with Dermoscopic Images
title_short Melanoma Classification Using a Novel Deep Convolutional Neural Network with Dermoscopic Images
title_sort melanoma classification using a novel deep convolutional neural network with dermoscopic images
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8838143/
https://www.ncbi.nlm.nih.gov/pubmed/35161878
http://dx.doi.org/10.3390/s22031134
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