Deep Hybrid Convolutional Neural Network for Segmentation of Melanoma Skin Lesion

Melanoma is a type of skin cancer that often leads to poor prognostic responses and survival rates. Melanoma usually develops in the limbs, including in fingers, palms, and the margins of the nails. When melanoma is detected early, surgical treatment may achieve a higher cure rate. The early diagnos...

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Autores principales:	Cheng-Hong Yang, Jai-Hong Ren, Hsiu-Chen Huang, Li-Yeh Chuang, Po-Yin Chang
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/4f918cb3d1004e2ab6efe45a5cc2fb1e
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spelling	oai:doaj.org-article:4f918cb3d1004e2ab6efe45a5cc2fb1e2021-11-22T01:11:34ZDeep Hybrid Convolutional Neural Network for Segmentation of Melanoma Skin Lesion1687-527310.1155/2021/9409508https://doaj.org/article/4f918cb3d1004e2ab6efe45a5cc2fb1e2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/9409508https://doaj.org/toc/1687-5273Melanoma is a type of skin cancer that often leads to poor prognostic responses and survival rates. Melanoma usually develops in the limbs, including in fingers, palms, and the margins of the nails. When melanoma is detected early, surgical treatment may achieve a higher cure rate. The early diagnosis of melanoma depends on the manual segmentation of suspected lesions. However, manual segmentation can lead to problems, including misclassification and low efficiency. Therefore, it is essential to devise a method for automatic image segmentation that overcomes the aforementioned issues. In this study, an improved algorithm is proposed, termed EfficientUNet++, which is developed from the U-Net model. In EfficientUNet++, the pretrained EfficientNet model is added to the UNet++ model to accelerate segmentation process, leading to more reliable and precise results in skin cancer image segmentation. Two skin lesion datasets were used to compare the performance of the proposed EfficientUNet++ algorithm with other common models. In the PH2 dataset, EfficientUNet++ achieved a better Dice coefficient (93% vs. 76%–91%), Intersection over Union (IoU, 96% vs. 74%–95%), and loss value (30% vs. 44%–32%) compared with other models. In the International Skin Imaging Collaboration dataset, EfficientUNet++ obtained a similar Dice coefficient (96% vs. 94%–96%) but a better IoU (94% vs. 89%–93%) and loss value (11% vs. 13%–11%) than other models. In conclusion, the EfficientUNet++ model efficiently detects skin lesions by improving composite coefficients and structurally expanding the size of the convolution network. Moreover, the use of residual units deepens the network to further improve performance.Cheng-Hong YangJai-Hong RenHsiu-Chen HuangLi-Yeh ChuangPo-Yin ChangHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7Neurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENComputational Intelligence and Neuroscience, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Cheng-Hong Yang Jai-Hong Ren Hsiu-Chen Huang Li-Yeh Chuang Po-Yin Chang Deep Hybrid Convolutional Neural Network for Segmentation of Melanoma Skin Lesion
description	Melanoma is a type of skin cancer that often leads to poor prognostic responses and survival rates. Melanoma usually develops in the limbs, including in fingers, palms, and the margins of the nails. When melanoma is detected early, surgical treatment may achieve a higher cure rate. The early diagnosis of melanoma depends on the manual segmentation of suspected lesions. However, manual segmentation can lead to problems, including misclassification and low efficiency. Therefore, it is essential to devise a method for automatic image segmentation that overcomes the aforementioned issues. In this study, an improved algorithm is proposed, termed EfficientUNet++, which is developed from the U-Net model. In EfficientUNet++, the pretrained EfficientNet model is added to the UNet++ model to accelerate segmentation process, leading to more reliable and precise results in skin cancer image segmentation. Two skin lesion datasets were used to compare the performance of the proposed EfficientUNet++ algorithm with other common models. In the PH2 dataset, EfficientUNet++ achieved a better Dice coefficient (93% vs. 76%–91%), Intersection over Union (IoU, 96% vs. 74%–95%), and loss value (30% vs. 44%–32%) compared with other models. In the International Skin Imaging Collaboration dataset, EfficientUNet++ obtained a similar Dice coefficient (96% vs. 94%–96%) but a better IoU (94% vs. 89%–93%) and loss value (11% vs. 13%–11%) than other models. In conclusion, the EfficientUNet++ model efficiently detects skin lesions by improving composite coefficients and structurally expanding the size of the convolution network. Moreover, the use of residual units deepens the network to further improve performance.
format	article
author	Cheng-Hong Yang Jai-Hong Ren Hsiu-Chen Huang Li-Yeh Chuang Po-Yin Chang
author_facet	Cheng-Hong Yang Jai-Hong Ren Hsiu-Chen Huang Li-Yeh Chuang Po-Yin Chang
author_sort	Cheng-Hong Yang
title	Deep Hybrid Convolutional Neural Network for Segmentation of Melanoma Skin Lesion
title_short	Deep Hybrid Convolutional Neural Network for Segmentation of Melanoma Skin Lesion
title_full	Deep Hybrid Convolutional Neural Network for Segmentation of Melanoma Skin Lesion
title_fullStr	Deep Hybrid Convolutional Neural Network for Segmentation of Melanoma Skin Lesion
title_full_unstemmed	Deep Hybrid Convolutional Neural Network for Segmentation of Melanoma Skin Lesion
title_sort	deep hybrid convolutional neural network for segmentation of melanoma skin lesion
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/4f918cb3d1004e2ab6efe45a5cc2fb1e
work_keys_str_mv	AT chenghongyang deephybridconvolutionalneuralnetworkforsegmentationofmelanomaskinlesion AT jaihongren deephybridconvolutionalneuralnetworkforsegmentationofmelanomaskinlesion AT hsiuchenhuang deephybridconvolutionalneuralnetworkforsegmentationofmelanomaskinlesion AT liyehchuang deephybridconvolutionalneuralnetworkforsegmentationofmelanomaskinlesion AT poyinchang deephybridconvolutionalneuralnetworkforsegmentationofmelanomaskinlesion
_version_	1718418280720367616

Deep Hybrid Convolutional Neural Network for Segmentation of Melanoma Skin Lesion

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