A Study on Diagnostic Assist Systems of Chronic Obstructive Pulmonary Disease from Medical Images by Deep Learning

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ISSN Print: 2327-5219
ISSN Online: 2327-5227
www.scirp.org/journal/jcc
E-mail: jcc@scirp.org

"A Study on Diagnostic Assist Systems of Chronic Obstructive Pulmonary Disease from Medical Images by Deep Learning"
written by Toru Kimura, Takashi Kawakami, Akihiro Kikuchi, Ryosuke Ooev, Masaki Akiyama, Hiroyuki Horikoshi,
published by Journal of Computer and Communications, Vol.6 No.1, 2018

has been cited by the following article(s):

[1]	Healthcare Techniques Through Deep Learning: Issues, Challenges and Opportunities
	2021

[2]	Diagnostic performance of dual-energy subtraction radiography for the detection of pulmonary emphysema: an intra-individual comparison
	Diagnostics, 2021

[3]	Recognition of lung volume condition based on phase space mapping using electrical impedance tomography
	2019

[4]	A Novel Hybrid Framework for the Detection and Risk Severity of Chronic Obstructive Pulmonary Disease
	2018

[5]	Development of Computer-Aided Diagnosis System to Evaluate the Lung Efficiency Using Electrical Impedance Tomography
	3rd International Congress on Electrical Engineering, 2018

[1]	Diagnostic Performance of Dual-Energy Subtraction Radiography for the Detection of Pulmonary Emphysema: An Intra-Individual Comparison Diagnostics, 2021 DOI:10.3390/diagnostics11101849

[2]	Healthcare Techniques Through Deep Learning: Issues, Challenges and Opportunities IEEE Access, 2021 DOI:10.1109/ACCESS.2021.3095312

[3]	Recognition of lung volume condition based on phase space mapping using electrical impedance tomography Journal of Electrical Bioimpedance, 2019 DOI:10.2478/joeb-2019-0005

[4]	Recognition of lung volume condition based on phase space mapping using electrical impedance tomography Journal of Electrical Bioimpedance, 2019 DOI:10.2478/joeb-2019-0005

[5]	Recognition of lung volume condition based on phase space mapping using electrical impedance tomography Journal of Electrical Bioimpedance, 2019 DOI:10.2478/joeb-2019-0005

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