NDLI: Neural network and fuzzy clustering approach for automatic diagnosis of coronary artery disease in nuclear medicine

Content Provider	IEEE Xplore Digital Library
Author	Bagher-Ebadian, H. Soltanian-Zadeh, H. Setayeshi, S. Smith, S.T.
Copyright Year	1963
Abstract	We investigated feasibility of using fuzzy clustering and artificial neural network to predict coronary artery disease (CAD) in acute phase from planar and gated SPECT nuclear medicine images. We developed an automatic computerized scheme that helps physicians diagnose coronary artery disease based on 99Tc-Sestamibi myocardial perfusion images. Our study consisted of two separate studies with respect to patient population and imaging method. The first study included 58 subjects (30 male, 28 female) studies using planar rest and stress imaging and a second patient subset of 115 subjects (61 male, 54 female) using gated rest/stress SPECT imaging. After the myocardial perfusion scans, patients also had coronary angiography within three months of the imaging. Signal-to-noise ratio was improved by segmentation of myocardium from its background in both studies using fuzzy clustering with the Picard iteration algorithm. We extracted a set of adaptive features consistent with nature of nuclear medicine imaging and myocardium anatomy. Features were optimized and selected based on maximum separation in multidimensional feature space. A back-propagation artificial neural network (ANN) classifier was trained and tested for each study using the optimal features and the results of coronary angiographies as input and outputs, respectively. ANNs were trained, optimized, and tested using leave-one-out and Poh's Implementation of Weigned-Rumelhart-Huberman (PIWRH) methods, to diagnose the normal and abnormal patients based on their coronary angiograms. The performances of the optimal ANNs were analyzed by receiver operator characteristic (ROC) method. Results of ANN in the first study were compared to those of the physicians in nuclear medicine ward and two other physicians using ROC method. Results of ANN for the second study were compared to those of the nuclear medicine ward using ROC method. Both subsets demonstrate that the proposed method outperforms visual diagnosis and is therefore a useful adjunct for CAD diagnosis from planar and gated SPECT images.
Sponsorship	IEEE Nuclear and Plasma Sciences Society Computer Applications in Nuclear and Plasma Sciences (CANPS) Lawrence Berkeley Lab. Lawrence Livermore Nat. Lab. APS College of William and Mary Continuous Electron Beam Accelerator Facility NASA Defence Nuclear Agency Sandia National Laboratories Jet Propulsion Laboratory Brookhaven Nat. Lab. Lawrence Livermore Nat. Lab IEEE/NPPS Radiat. Effects Committee Defence Nuclear Agency/DoD Sandia National Laboratories/DOE Jet Propulsion Laboratory/NASA Phillips Lab./DoD
Starting Page	184
Ending Page	192
Page Count	9
File Size	750478
File Format	PDF
ISSN	00189499
Volume Number	51
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-02-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Fuzzy neural networks Coronary arteriosclerosis Nuclear medicine Artificial neural networks Myocardium Stress Angiography Testing Physics computing
Content Type	Text
Resource Type	Article
Subject	Nuclear and High Energy Physics Electrical and Electronic Engineering Nuclear Energy and Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Neural network approach for the computer-aided diagnosis of coronary artery diseases in nuclear medicine

Automatic Assessment of Cardiac Artery Disease by Using DCAD Module

Current approach to the diagnosis of atherosclerotic coronary artery disease: more questions than answers

Artificial neural network for the exercise electrocardiographic detection of coronary artery disease

Neural networks in detection of coronary artery disease

Diagnostic Performance of Coronary CT Angiography, Stress Dual-Energy CT Perfusion, and Stress Perfusion Single-Photon Emission Computed Tomography for Coronary Artery Disease: Comparison with Combined Invasive Coronary Angiography and Stress Perfusion Cardiac MRI.

FCM-DNN: diagnosing coronary artery disease by deep accuracy fuzzy C-means clustering model

Artificial Neural Networks Prognostic Evaluation of Post-Surgery Complications in Patients Underwent to Coronary Artery Bypass Graft Surgery

Noninvasive detection of coronary artery disease

Neural network and fuzzy clustering approach for automatic diagnosis of coronary artery disease in nuclear medicine

Similar Documents

Neural network approach for the computer-aided diagnosis of coronary artery diseases in nuclear medicine

Automatic Assessment of Cardiac Artery Disease by Using DCAD Module

Current approach to the diagnosis of atherosclerotic coronary artery disease: more questions than answers

Artificial neural network for the exercise electrocardiographic detection of coronary artery disease

Neural networks in detection of coronary artery disease

Diagnostic Performance of Coronary CT Angiography, Stress Dual-Energy CT Perfusion, and Stress Perfusion Single-Photon Emission Computed Tomography for Coronary Artery Disease: Comparison with Combined Invasive Coronary Angiography and Stress Perfusion Cardiac MRI.

FCM-DNN: diagnosing coronary artery disease by deep accuracy fuzzy C-means clustering model

Artificial Neural Networks Prognostic Evaluation of Post-Surgery Complications in Patients Underwent to Coronary Artery Bypass Graft Surgery

Noninvasive detection of coronary artery disease

Neural network and fuzzy clustering approach for automatic diagnosis of coronary artery disease in nuclear medicine