Multi-Level Cross Residual Network for Lung Nodule Classification.

Lyu, J; Bi, X; Ling, SH

Multi-Level Cross Residual Network for Lung Nodule Classification.

Lyu, J Bi, X Ling, SH

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Sensors (Basel, Switzerland), 2020, 20, (10)
Issue Date:: 2020-05-16

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Field	Value	Language
dc.contributor.author	Lyu, J
dc.contributor.author	Bi, X
dc.contributor.author	Ling, SH
dc.date.accessioned	2020-11-03T01:29:28Z
dc.date.available	2020-05-14
dc.date.available	2020-11-03T01:29:28Z
dc.date.issued	2020-05-16
dc.identifier.citation	Sensors (Basel, Switzerland), 2020, 20, (10)
dc.identifier.issn	1424-8220
dc.identifier.issn	1424-8220
dc.identifier.uri	http://hdl.handle.net/10453/143713
dc.description.abstract	Computer-aided algorithm plays an important role in disease diagnosis through medical images. As one of the major cancers, lung cancer is commonly detected by computer tomography. To increase the survival rate of lung cancer patients, an early-stage diagnosis is necessary. In this paper, we propose a new structure, multi-level cross residual convolutional neural network (ML-xResNet), to classify the different types of lung nodule malignancies. ML-xResNet is constructed by three-level parallel ResNets with different convolution kernel sizes to extract multi-scale features of the inputs. Moreover, the residuals are connected not only with the current level but also with other levels in a crossover manner. To illustrate the performance of ML-xResNet, we apply the model to process ternary classification (benign, indeterminate, and malignant lung nodules) and binary classification (benign and malignant lung nodules) of lung nodules, respectively. Based on the experiment results, the proposed ML-xResNet achieves the best results of 85.88% accuracy for ternary classification and 92.19% accuracy for binary classification, without any additional handcrafted preprocessing algorithm.
dc.format	Electronic
dc.language	eng
dc.publisher	MDPI
dc.relation.ispartof	Sensors (Basel, Switzerland)
dc.relation.isbasedon	10.3390/s20102837
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0301 Analytical Chemistry, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 0502 Environmental Science and Management, 0602 Ecology
dc.subject.classification	Analytical Chemistry
dc.title	Multi-Level Cross Residual Network for Lung Nodule Classification.
dc.type	Journal Article
utslib.citation.volume	20
utslib.location.activity	Switzerland
utslib.for	0301 Analytical Chemistry
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0502 Environmental Science and Management
utslib.for	0602 Ecology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
dc.date.updated	2020-11-03T01:29:18Z
pubs.issue	10
pubs.publication-status	Published
pubs.volume	20
utslib.citation.issue	10

Abstract:

Computer-aided algorithm plays an important role in disease diagnosis through medical images. As one of the major cancers, lung cancer is commonly detected by computer tomography. To increase the survival rate of lung cancer patients, an early-stage diagnosis is necessary. In this paper, we propose a new structure, multi-level cross residual convolutional neural network (ML-xResNet), to classify the different types of lung nodule malignancies. ML-xResNet is constructed by three-level parallel ResNets with different convolution kernel sizes to extract multi-scale features of the inputs. Moreover, the residuals are connected not only with the current level but also with other levels in a crossover manner. To illustrate the performance of ML-xResNet, we apply the model to process ternary classification (benign, indeterminate, and malignant lung nodules) and binary classification (benign and malignant lung nodules) of lung nodules, respectively. Based on the experiment results, the proposed ML-xResNet achieves the best results of 85.88% accuracy for ternary classification and 92.19% accuracy for binary classification, without any additional handcrafted preprocessing algorithm.

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http://hdl.handle.net/10453/143713