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Two-Stage Deep Neural Network via Ensemble Learning for Melanoma Classification

Melanoma is a skin disease with a high fatality rate. Early diagnosis of melanoma can effectively increase the survival rate of patients. There are three types of dermoscopy images, malignant melanoma, benign nevis, and seborrheic keratosis, so using dermoscopy images to classify melanoma is an indi...

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Autores principales: Ding, Jiaqi, Song , Jie, Li, Jiawei, Tang, Jijun, Guo, Fei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8804371/
https://www.ncbi.nlm.nih.gov/pubmed/35118054
http://dx.doi.org/10.3389/fbioe.2021.758495
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author Ding, Jiaqi
Song , Jie
Li, Jiawei
Tang, Jijun
Guo, Fei
author_facet Ding, Jiaqi
Song , Jie
Li, Jiawei
Tang, Jijun
Guo, Fei
author_sort Ding, Jiaqi
collection PubMed
description Melanoma is a skin disease with a high fatality rate. Early diagnosis of melanoma can effectively increase the survival rate of patients. There are three types of dermoscopy images, malignant melanoma, benign nevis, and seborrheic keratosis, so using dermoscopy images to classify melanoma is an indispensable task in diagnosis. However, early melanoma classification works can only use the low-level information of images, so the melanoma cannot be classified efficiently; the recent deep learning methods mainly depend on a single network, although it can extract high-level features, the poor scale and type of the features limited the results of the classification. Therefore, we need an automatic classification method for melanoma, which can make full use of the rich and deep feature information of images for classification. In this study, we propose an ensemble method that can integrate different types of classification networks for melanoma classification. Specifically, we first use U-net to segment the lesion area of images to generate a lesion mask, thus resize images to focus on the lesion; then, we use five excellent classification models to classify dermoscopy images, and adding squeeze-excitation block (SE block) to models to emphasize the more informative features; finally, we use our proposed new ensemble network to integrate five different classification results. The experimental results prove the validity of our results. We test our method on the ISIC 2017 challenge dataset and obtain excellent results on multiple metrics; especially, we get 0.909 on accuracy. Our classification framework can provide an efficient and accurate way for melanoma classification using dermoscopy images, laying the foundation for early diagnosis and later treatment of melanoma.
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spelling pubmed-88043712022-02-02 Two-Stage Deep Neural Network via Ensemble Learning for Melanoma Classification Ding, Jiaqi Song , Jie Li, Jiawei Tang, Jijun Guo, Fei Front Bioeng Biotechnol Bioengineering and Biotechnology Melanoma is a skin disease with a high fatality rate. Early diagnosis of melanoma can effectively increase the survival rate of patients. There are three types of dermoscopy images, malignant melanoma, benign nevis, and seborrheic keratosis, so using dermoscopy images to classify melanoma is an indispensable task in diagnosis. However, early melanoma classification works can only use the low-level information of images, so the melanoma cannot be classified efficiently; the recent deep learning methods mainly depend on a single network, although it can extract high-level features, the poor scale and type of the features limited the results of the classification. Therefore, we need an automatic classification method for melanoma, which can make full use of the rich and deep feature information of images for classification. In this study, we propose an ensemble method that can integrate different types of classification networks for melanoma classification. Specifically, we first use U-net to segment the lesion area of images to generate a lesion mask, thus resize images to focus on the lesion; then, we use five excellent classification models to classify dermoscopy images, and adding squeeze-excitation block (SE block) to models to emphasize the more informative features; finally, we use our proposed new ensemble network to integrate five different classification results. The experimental results prove the validity of our results. We test our method on the ISIC 2017 challenge dataset and obtain excellent results on multiple metrics; especially, we get 0.909 on accuracy. Our classification framework can provide an efficient and accurate way for melanoma classification using dermoscopy images, laying the foundation for early diagnosis and later treatment of melanoma. Frontiers Media S.A. 2022-01-18 /pmc/articles/PMC8804371/ /pubmed/35118054 http://dx.doi.org/10.3389/fbioe.2021.758495 Text en Copyright © 2022 Ding, Song , Li, Tang and Guo. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Bioengineering and Biotechnology
Ding, Jiaqi
Song , Jie
Li, Jiawei
Tang, Jijun
Guo, Fei
Two-Stage Deep Neural Network via Ensemble Learning for Melanoma Classification
title Two-Stage Deep Neural Network via Ensemble Learning for Melanoma Classification
title_full Two-Stage Deep Neural Network via Ensemble Learning for Melanoma Classification
title_fullStr Two-Stage Deep Neural Network via Ensemble Learning for Melanoma Classification
title_full_unstemmed Two-Stage Deep Neural Network via Ensemble Learning for Melanoma Classification
title_short Two-Stage Deep Neural Network via Ensemble Learning for Melanoma Classification
title_sort two-stage deep neural network via ensemble learning for melanoma classification
topic Bioengineering and Biotechnology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8804371/
https://www.ncbi.nlm.nih.gov/pubmed/35118054
http://dx.doi.org/10.3389/fbioe.2021.758495
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