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Convolutional neural networks for the detection of malignant melanoma in dermoscopy images
INTRODUCTION: Convolutional neural networks gained popularity due to their ability to detect and classify objects in images and videos. It gives also an opportunity to use them for medical tasks in such specialties like dermatology, radiology or ophthalmology. The aim of this study was to investigat...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Termedia Publishing House
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8330874/ https://www.ncbi.nlm.nih.gov/pubmed/34377121 http://dx.doi.org/10.5114/ada.2021.107927 |
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author | Kwiatkowska, Dominika Kluska, Piotr Reich, Adam |
author_facet | Kwiatkowska, Dominika Kluska, Piotr Reich, Adam |
author_sort | Kwiatkowska, Dominika |
collection | PubMed |
description | INTRODUCTION: Convolutional neural networks gained popularity due to their ability to detect and classify objects in images and videos. It gives also an opportunity to use them for medical tasks in such specialties like dermatology, radiology or ophthalmology. The aim of this study was to investigate the ability of convolutional neural networks to classify malignant melanoma in dermoscopy images. AIM: To examine the usefulness of deep learning models in malignant melanoma detection based on dermoscopy images. MATERIAL AND METHODS: Four convolutional neural networks were trained on open source dataset containing dermoscopy images of seven types of skin lesions. To evaluate the performance of artificial neural networks, the precision, sensitivity, F1 score, specificity and area under the receiver operating curve were calculated. In addition, an ensemble of all neural networks’ ability of proper malignant melanoma classification was compared with the results achieved by every single network. RESULTS: The best convolutional neural network achieved on average 0.88 precision, 0.83 sensitivity, 0.85 F1 score and 0.99 specificity in the classification of all skin lesion types. CONCLUSIONS: Artificial neural networks might be helpful in malignant melanoma detection in dermoscopy images. |
format | Online Article Text |
id | pubmed-8330874 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Termedia Publishing House |
record_format | MEDLINE/PubMed |
spelling | pubmed-83308742021-08-09 Convolutional neural networks for the detection of malignant melanoma in dermoscopy images Kwiatkowska, Dominika Kluska, Piotr Reich, Adam Postepy Dermatol Alergol Original Paper INTRODUCTION: Convolutional neural networks gained popularity due to their ability to detect and classify objects in images and videos. It gives also an opportunity to use them for medical tasks in such specialties like dermatology, radiology or ophthalmology. The aim of this study was to investigate the ability of convolutional neural networks to classify malignant melanoma in dermoscopy images. AIM: To examine the usefulness of deep learning models in malignant melanoma detection based on dermoscopy images. MATERIAL AND METHODS: Four convolutional neural networks were trained on open source dataset containing dermoscopy images of seven types of skin lesions. To evaluate the performance of artificial neural networks, the precision, sensitivity, F1 score, specificity and area under the receiver operating curve were calculated. In addition, an ensemble of all neural networks’ ability of proper malignant melanoma classification was compared with the results achieved by every single network. RESULTS: The best convolutional neural network achieved on average 0.88 precision, 0.83 sensitivity, 0.85 F1 score and 0.99 specificity in the classification of all skin lesion types. CONCLUSIONS: Artificial neural networks might be helpful in malignant melanoma detection in dermoscopy images. Termedia Publishing House 2021-07-26 2021-06 /pmc/articles/PMC8330874/ /pubmed/34377121 http://dx.doi.org/10.5114/ada.2021.107927 Text en Copyright: © 2021 Termedia Sp. z o. o. https://creativecommons.org/licenses/by-nc-nd/3.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License, permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Paper Kwiatkowska, Dominika Kluska, Piotr Reich, Adam Convolutional neural networks for the detection of malignant melanoma in dermoscopy images |
title | Convolutional neural networks for the detection of malignant melanoma in dermoscopy images |
title_full | Convolutional neural networks for the detection of malignant melanoma in dermoscopy images |
title_fullStr | Convolutional neural networks for the detection of malignant melanoma in dermoscopy images |
title_full_unstemmed | Convolutional neural networks for the detection of malignant melanoma in dermoscopy images |
title_short | Convolutional neural networks for the detection of malignant melanoma in dermoscopy images |
title_sort | convolutional neural networks for the detection of malignant melanoma in dermoscopy images |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8330874/ https://www.ncbi.nlm.nih.gov/pubmed/34377121 http://dx.doi.org/10.5114/ada.2021.107927 |
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