Liver Cancer Detection Using Hybridized Fully Convolutional Neural Network Based on Deep Learning Framework

Liver cancer is one of the world's largest causes of death to humans. It is a difficult task and time consuming to identify the cancer tissue manually in the present scenario. The segmentation of liver lesions in CT images can be used to assess the tumor load, plan treatments predict, and monit...
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Autor*in:	Xin Dong [verfasserIn] Yizhao Zhou [verfasserIn] Lantian Wang [verfasserIn] Jingfeng Peng [verfasserIn] Yanbo Lou [verfasserIn] Yiqun Fan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Liver cancer detection deep learning fully convolutional neural network

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 8(2020), Seite 129889-129898
Übergeordnetes Werk:	volume:8 ; year:2020 ; pages:129889-129898

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2020.3006362

Katalog-ID:	DOAJ071775897

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spelling	10.1109/ACCESS.2020.3006362 doi (DE-627)DOAJ071775897 (DE-599)DOAJac8bcf9c1f0241d987721444d5653873 DE-627 ger DE-627 rakwb eng TK1-9971 Xin Dong verfasserin aut Liver Cancer Detection Using Hybridized Fully Convolutional Neural Network Based on Deep Learning Framework 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Liver cancer is one of the world's largest causes of death to humans. It is a difficult task and time consuming to identify the cancer tissue manually in the present scenario. The segmentation of liver lesions in CT images can be used to assess the tumor load, plan treatments predict, and monitor the clinical response. In this paper, the Hybridized Fully Convolutional Neural Network (HFCNN) has been proposed for liver tumor segmentation, which has been modeled mathematically to resolve the current issue of liver cancer. For semantic segmentation, HFCNN has been used as a powerful tool for liver cancer analysis. Whereas the CT-based lesion-type definition defines the diagnosis and therapeutic strategy, the distinction between cancer and non-cancer lesions is crucial. It demands highly qualified experience, expertise, and resources. However, a deep end-to-end learning approach to help discrimination in abdominal CT images of the liver between liver metastases of colorectal cancer and benign cysts has been analyzed. Our method includes the successful extraction of features from Inception combined with residual and pre-trained weights. Feature maps have been consistent with the original image voxel features, and The importance of features seemed to represent the most relevant imaging criteria for every class. This deep learning system shows the concept of illumination portions of the decision-making process of a pre-trained deep neural network, through an analysis of inner layers and the description of features that lead to predictions. Liver cancer detection deep learning fully convolutional neural network Electrical engineering. Electronics. Nuclear engineering Yizhao Zhou verfasserin aut Lantian Wang verfasserin aut Jingfeng Peng verfasserin aut Yanbo Lou verfasserin aut Yiqun Fan verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 129889-129898 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:129889-129898 https://doi.org/10.1109/ACCESS.2020.3006362 kostenfrei https://doaj.org/article/ac8bcf9c1f0241d987721444d5653873 kostenfrei https://ieeexplore.ieee.org/document/9130662/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_2014 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 129889-129898
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allfieldsSound	10.1109/ACCESS.2020.3006362 doi (DE-627)DOAJ071775897 (DE-599)DOAJac8bcf9c1f0241d987721444d5653873 DE-627 ger DE-627 rakwb eng TK1-9971 Xin Dong verfasserin aut Liver Cancer Detection Using Hybridized Fully Convolutional Neural Network Based on Deep Learning Framework 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Liver cancer is one of the world's largest causes of death to humans. It is a difficult task and time consuming to identify the cancer tissue manually in the present scenario. The segmentation of liver lesions in CT images can be used to assess the tumor load, plan treatments predict, and monitor the clinical response. In this paper, the Hybridized Fully Convolutional Neural Network (HFCNN) has been proposed for liver tumor segmentation, which has been modeled mathematically to resolve the current issue of liver cancer. For semantic segmentation, HFCNN has been used as a powerful tool for liver cancer analysis. Whereas the CT-based lesion-type definition defines the diagnosis and therapeutic strategy, the distinction between cancer and non-cancer lesions is crucial. It demands highly qualified experience, expertise, and resources. However, a deep end-to-end learning approach to help discrimination in abdominal CT images of the liver between liver metastases of colorectal cancer and benign cysts has been analyzed. Our method includes the successful extraction of features from Inception combined with residual and pre-trained weights. Feature maps have been consistent with the original image voxel features, and The importance of features seemed to represent the most relevant imaging criteria for every class. This deep learning system shows the concept of illumination portions of the decision-making process of a pre-trained deep neural network, through an analysis of inner layers and the description of features that lead to predictions. Liver cancer detection deep learning fully convolutional neural network Electrical engineering. Electronics. Nuclear engineering Yizhao Zhou verfasserin aut Lantian Wang verfasserin aut Jingfeng Peng verfasserin aut Yanbo Lou verfasserin aut Yiqun Fan verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 129889-129898 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:129889-129898 https://doi.org/10.1109/ACCESS.2020.3006362 kostenfrei https://doaj.org/article/ac8bcf9c1f0241d987721444d5653873 kostenfrei https://ieeexplore.ieee.org/document/9130662/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_2014 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 129889-129898
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topic_facet	Liver cancer detection deep learning fully convolutional neural network Electrical engineering. Electronics. Nuclear engineering
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authorswithroles_txt_mv	Xin Dong @@aut@@ Yizhao Zhou @@aut@@ Lantian Wang @@aut@@ Jingfeng Peng @@aut@@ Yanbo Lou @@aut@@ Yiqun Fan @@aut@@
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callnumber-first	T - Technology
author	Xin Dong
spellingShingle	Xin Dong misc TK1-9971 misc Liver cancer detection misc deep learning misc fully convolutional neural network misc Electrical engineering. Electronics. Nuclear engineering Liver Cancer Detection Using Hybridized Fully Convolutional Neural Network Based on Deep Learning Framework
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topic_title	TK1-9971 Liver Cancer Detection Using Hybridized Fully Convolutional Neural Network Based on Deep Learning Framework Liver cancer detection deep learning fully convolutional neural network
topic	misc TK1-9971 misc Liver cancer detection misc deep learning misc fully convolutional neural network misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Liver cancer detection misc deep learning misc fully convolutional neural network misc Electrical engineering. Electronics. Nuclear engineering
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title	Liver Cancer Detection Using Hybridized Fully Convolutional Neural Network Based on Deep Learning Framework
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title_sort	liver cancer detection using hybridized fully convolutional neural network based on deep learning framework
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title_auth	Liver Cancer Detection Using Hybridized Fully Convolutional Neural Network Based on Deep Learning Framework
abstract	Liver cancer is one of the world's largest causes of death to humans. It is a difficult task and time consuming to identify the cancer tissue manually in the present scenario. The segmentation of liver lesions in CT images can be used to assess the tumor load, plan treatments predict, and monitor the clinical response. In this paper, the Hybridized Fully Convolutional Neural Network (HFCNN) has been proposed for liver tumor segmentation, which has been modeled mathematically to resolve the current issue of liver cancer. For semantic segmentation, HFCNN has been used as a powerful tool for liver cancer analysis. Whereas the CT-based lesion-type definition defines the diagnosis and therapeutic strategy, the distinction between cancer and non-cancer lesions is crucial. It demands highly qualified experience, expertise, and resources. However, a deep end-to-end learning approach to help discrimination in abdominal CT images of the liver between liver metastases of colorectal cancer and benign cysts has been analyzed. Our method includes the successful extraction of features from Inception combined with residual and pre-trained weights. Feature maps have been consistent with the original image voxel features, and The importance of features seemed to represent the most relevant imaging criteria for every class. This deep learning system shows the concept of illumination portions of the decision-making process of a pre-trained deep neural network, through an analysis of inner layers and the description of features that lead to predictions.
abstractGer	Liver cancer is one of the world's largest causes of death to humans. It is a difficult task and time consuming to identify the cancer tissue manually in the present scenario. The segmentation of liver lesions in CT images can be used to assess the tumor load, plan treatments predict, and monitor the clinical response. In this paper, the Hybridized Fully Convolutional Neural Network (HFCNN) has been proposed for liver tumor segmentation, which has been modeled mathematically to resolve the current issue of liver cancer. For semantic segmentation, HFCNN has been used as a powerful tool for liver cancer analysis. Whereas the CT-based lesion-type definition defines the diagnosis and therapeutic strategy, the distinction between cancer and non-cancer lesions is crucial. It demands highly qualified experience, expertise, and resources. However, a deep end-to-end learning approach to help discrimination in abdominal CT images of the liver between liver metastases of colorectal cancer and benign cysts has been analyzed. Our method includes the successful extraction of features from Inception combined with residual and pre-trained weights. Feature maps have been consistent with the original image voxel features, and The importance of features seemed to represent the most relevant imaging criteria for every class. This deep learning system shows the concept of illumination portions of the decision-making process of a pre-trained deep neural network, through an analysis of inner layers and the description of features that lead to predictions.
abstract_unstemmed	Liver cancer is one of the world's largest causes of death to humans. It is a difficult task and time consuming to identify the cancer tissue manually in the present scenario. The segmentation of liver lesions in CT images can be used to assess the tumor load, plan treatments predict, and monitor the clinical response. In this paper, the Hybridized Fully Convolutional Neural Network (HFCNN) has been proposed for liver tumor segmentation, which has been modeled mathematically to resolve the current issue of liver cancer. For semantic segmentation, HFCNN has been used as a powerful tool for liver cancer analysis. Whereas the CT-based lesion-type definition defines the diagnosis and therapeutic strategy, the distinction between cancer and non-cancer lesions is crucial. It demands highly qualified experience, expertise, and resources. However, a deep end-to-end learning approach to help discrimination in abdominal CT images of the liver between liver metastases of colorectal cancer and benign cysts has been analyzed. Our method includes the successful extraction of features from Inception combined with residual and pre-trained weights. Feature maps have been consistent with the original image voxel features, and The importance of features seemed to represent the most relevant imaging criteria for every class. This deep learning system shows the concept of illumination portions of the decision-making process of a pre-trained deep neural network, through an analysis of inner layers and the description of features that lead to predictions.
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title_short	Liver Cancer Detection Using Hybridized Fully Convolutional Neural Network Based on Deep Learning Framework
url	https://doi.org/10.1109/ACCESS.2020.3006362 https://doaj.org/article/ac8bcf9c1f0241d987721444d5653873 https://ieeexplore.ieee.org/document/9130662/ https://doaj.org/toc/2169-3536
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