Lung Nodule CT Image Segmentation Model Based on Multiscale Dense Residual Neural Network

To solve the problem of the low segmentation accuracy of lung nodule CT images using U-Net, an improved method for segmentation of lung nodules by U-Net was proposed. Initially, the dense network connection and sawtooth expanded convolution design was added to the feature extraction part, and a loca...
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Autor*in:	Xinying Zhang [verfasserIn] Shanshan Kong [verfasserIn] Yang Han [verfasserIn] Baoshan Xie [verfasserIn] Chunfeng Liu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	U-Net medical image segmentation DenseNet lung cancer

Übergeordnetes Werk:	In: Mathematics - MDPI AG, 2013, 11(2023), 6, p 1363
Übergeordnetes Werk:	volume:11 ; year:2023 ; number:6, p 1363

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/math11061363

Katalog-ID:	DOAJ087302357

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520			\|a To solve the problem of the low segmentation accuracy of lung nodule CT images using U-Net, an improved method for segmentation of lung nodules by U-Net was proposed. Initially, the dense network connection and sawtooth expanded convolution design was added to the feature extraction part, and a local residual design was adopted in the upsampling process. Finally, the effectiveness of the proposed algorithm was evaluated using the LIDC-IDRI lung nodule public dataset. The results showed that the improved algorithm had 7.03%, 14.05%, and 10.43% higher performance than the U-Net segmentation algorithm under the three loss functions of DC, MIOU, and SE, and the accuracy was 2.45% higher compared with that of U-Net. Thus, the proposed method had an effective network structure.
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allfieldsSound	10.3390/math11061363 doi (DE-627)DOAJ087302357 (DE-599)DOAJ270f13364ec54f35b5d6dc281f5c8baa DE-627 ger DE-627 rakwb eng QA1-939 Xinying Zhang verfasserin aut Lung Nodule CT Image Segmentation Model Based on Multiscale Dense Residual Neural Network 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To solve the problem of the low segmentation accuracy of lung nodule CT images using U-Net, an improved method for segmentation of lung nodules by U-Net was proposed. Initially, the dense network connection and sawtooth expanded convolution design was added to the feature extraction part, and a local residual design was adopted in the upsampling process. Finally, the effectiveness of the proposed algorithm was evaluated using the LIDC-IDRI lung nodule public dataset. The results showed that the improved algorithm had 7.03%, 14.05%, and 10.43% higher performance than the U-Net segmentation algorithm under the three loss functions of DC, MIOU, and SE, and the accuracy was 2.45% higher compared with that of U-Net. Thus, the proposed method had an effective network structure. U-Net medical image segmentation DenseNet lung cancer Mathematics Shanshan Kong verfasserin aut Yang Han verfasserin aut Baoshan Xie verfasserin aut Chunfeng Liu verfasserin aut In Mathematics MDPI AG, 2013 11(2023), 6, p 1363 (DE-627)737287764 (DE-600)2704244-3 22277390 nnns volume:11 year:2023 number:6, p 1363 https://doi.org/10.3390/math11061363 kostenfrei https://doaj.org/article/270f13364ec54f35b5d6dc281f5c8baa kostenfrei https://www.mdpi.com/2227-7390/11/6/1363 kostenfrei https://doaj.org/toc/2227-7390 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 6, p 1363
language	English
source	In Mathematics 11(2023), 6, p 1363 volume:11 year:2023 number:6, p 1363
sourceStr	In Mathematics 11(2023), 6, p 1363 volume:11 year:2023 number:6, p 1363
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topic_facet	U-Net medical image segmentation DenseNet lung cancer Mathematics
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authorswithroles_txt_mv	Xinying Zhang @@aut@@ Shanshan Kong @@aut@@ Yang Han @@aut@@ Baoshan Xie @@aut@@ Chunfeng Liu @@aut@@
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callnumber-first	Q - Science
author	Xinying Zhang
spellingShingle	Xinying Zhang misc QA1-939 misc U-Net misc medical image misc segmentation misc DenseNet misc lung cancer misc Mathematics Lung Nodule CT Image Segmentation Model Based on Multiscale Dense Residual Neural Network
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topic_title	QA1-939 Lung Nodule CT Image Segmentation Model Based on Multiscale Dense Residual Neural Network U-Net medical image segmentation DenseNet lung cancer
topic	misc QA1-939 misc U-Net misc medical image misc segmentation misc DenseNet misc lung cancer misc Mathematics
topic_unstemmed	misc QA1-939 misc U-Net misc medical image misc segmentation misc DenseNet misc lung cancer misc Mathematics
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title	Lung Nodule CT Image Segmentation Model Based on Multiscale Dense Residual Neural Network
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title_sort	lung nodule ct image segmentation model based on multiscale dense residual neural network
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title_auth	Lung Nodule CT Image Segmentation Model Based on Multiscale Dense Residual Neural Network
abstract	To solve the problem of the low segmentation accuracy of lung nodule CT images using U-Net, an improved method for segmentation of lung nodules by U-Net was proposed. Initially, the dense network connection and sawtooth expanded convolution design was added to the feature extraction part, and a local residual design was adopted in the upsampling process. Finally, the effectiveness of the proposed algorithm was evaluated using the LIDC-IDRI lung nodule public dataset. The results showed that the improved algorithm had 7.03%, 14.05%, and 10.43% higher performance than the U-Net segmentation algorithm under the three loss functions of DC, MIOU, and SE, and the accuracy was 2.45% higher compared with that of U-Net. Thus, the proposed method had an effective network structure.
abstractGer	To solve the problem of the low segmentation accuracy of lung nodule CT images using U-Net, an improved method for segmentation of lung nodules by U-Net was proposed. Initially, the dense network connection and sawtooth expanded convolution design was added to the feature extraction part, and a local residual design was adopted in the upsampling process. Finally, the effectiveness of the proposed algorithm was evaluated using the LIDC-IDRI lung nodule public dataset. The results showed that the improved algorithm had 7.03%, 14.05%, and 10.43% higher performance than the U-Net segmentation algorithm under the three loss functions of DC, MIOU, and SE, and the accuracy was 2.45% higher compared with that of U-Net. Thus, the proposed method had an effective network structure.
abstract_unstemmed	To solve the problem of the low segmentation accuracy of lung nodule CT images using U-Net, an improved method for segmentation of lung nodules by U-Net was proposed. Initially, the dense network connection and sawtooth expanded convolution design was added to the feature extraction part, and a local residual design was adopted in the upsampling process. Finally, the effectiveness of the proposed algorithm was evaluated using the LIDC-IDRI lung nodule public dataset. The results showed that the improved algorithm had 7.03%, 14.05%, and 10.43% higher performance than the U-Net segmentation algorithm under the three loss functions of DC, MIOU, and SE, and the accuracy was 2.45% higher compared with that of U-Net. Thus, the proposed method had an effective network structure.
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title_short	Lung Nodule CT Image Segmentation Model Based on Multiscale Dense Residual Neural Network
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