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Bucket of Deep Transfer Learning Features and Classification Models for Melanoma Detection

Malignant melanoma is the deadliest form of skin cancer and, in recent years, is rapidly growing in terms of the incidence worldwide rate. The most effective approach to targeted treatment is early diagnosis. Deep learning algorithms, specifically convolutional neural networks, represent a methodolo...

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Detalles Bibliográficos
Autores principales: Manzo, Mario, Pellino, Simone
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321205/
https://www.ncbi.nlm.nih.gov/pubmed/34460526
http://dx.doi.org/10.3390/jimaging6120129
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author Manzo, Mario
Pellino, Simone
author_facet Manzo, Mario
Pellino, Simone
author_sort Manzo, Mario
collection PubMed
description Malignant melanoma is the deadliest form of skin cancer and, in recent years, is rapidly growing in terms of the incidence worldwide rate. The most effective approach to targeted treatment is early diagnosis. Deep learning algorithms, specifically convolutional neural networks, represent a methodology for the image analysis and representation. They optimize the features design task, essential for an automatic approach on different types of images, including medical. In this paper, we adopted pretrained deep convolutional neural networks architectures for the image representation with purpose to predict skin lesion melanoma. Firstly, we applied a transfer learning approach to extract image features. Secondly, we adopted the transferred learning features inside an ensemble classification context. Specifically, the framework trains individual classifiers on balanced subspaces and combines the provided predictions through statistical measures. Experimental phase on datasets of skin lesion images is performed and results obtained show the effectiveness of the proposed approach with respect to state-of-the-art competitors.
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spelling pubmed-83212052021-08-26 Bucket of Deep Transfer Learning Features and Classification Models for Melanoma Detection Manzo, Mario Pellino, Simone J Imaging Article Malignant melanoma is the deadliest form of skin cancer and, in recent years, is rapidly growing in terms of the incidence worldwide rate. The most effective approach to targeted treatment is early diagnosis. Deep learning algorithms, specifically convolutional neural networks, represent a methodology for the image analysis and representation. They optimize the features design task, essential for an automatic approach on different types of images, including medical. In this paper, we adopted pretrained deep convolutional neural networks architectures for the image representation with purpose to predict skin lesion melanoma. Firstly, we applied a transfer learning approach to extract image features. Secondly, we adopted the transferred learning features inside an ensemble classification context. Specifically, the framework trains individual classifiers on balanced subspaces and combines the provided predictions through statistical measures. Experimental phase on datasets of skin lesion images is performed and results obtained show the effectiveness of the proposed approach with respect to state-of-the-art competitors. MDPI 2020-11-26 /pmc/articles/PMC8321205/ /pubmed/34460526 http://dx.doi.org/10.3390/jimaging6120129 Text en © 2020 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 (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ).
spellingShingle Article
Manzo, Mario
Pellino, Simone
Bucket of Deep Transfer Learning Features and Classification Models for Melanoma Detection
title Bucket of Deep Transfer Learning Features and Classification Models for Melanoma Detection
title_full Bucket of Deep Transfer Learning Features and Classification Models for Melanoma Detection
title_fullStr Bucket of Deep Transfer Learning Features and Classification Models for Melanoma Detection
title_full_unstemmed Bucket of Deep Transfer Learning Features and Classification Models for Melanoma Detection
title_short Bucket of Deep Transfer Learning Features and Classification Models for Melanoma Detection
title_sort bucket of deep transfer learning features and classification models for melanoma detection
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321205/
https://www.ncbi.nlm.nih.gov/pubmed/34460526
http://dx.doi.org/10.3390/jimaging6120129
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