A comparison of logistic regression analysis and an artificial neural network using the BI-RADS lexicon for ultrasonography in conjunction with introbserver variability

Journal of Digital Imaging

Volumn 25, Issue 5, 2012, Pages 599-606

  Kim, Sun Mi ^a,b   Han, Heon ^a   Park, Jeong Mi ^c   Choi, Yoon Jung ^d   Yoon, Hoi Soo ^a   Sohn, Jung Hee ^a   Baek, Moon Hee ^a   Kim, Yoon Nam ^e   Chae, Young Moon ^e   June, Jeon Jong ^f   Lee, Jiwon ^a   Jeon, Yong Hwan ^a  

^a Kangwon National University College of Medicine   (South Korea)

^b Seoul National University Bundang Hospital   (South Korea)

^c UNIVERSITY OF IOWA   (United States)

^d School of Medicine   (South Korea)

^e Yonsei University   (South Korea)

^f Seoul National University   (South Korea)

Author keywords

Artificial neural network; Breast; Breast neoplasm; Logistic regression; Ultrasonography

Indexed keywords

AREA UNDER THE ROC CURVE; BI-RADS; BREAST; BREAST CANCER; BREAST IMAGING; BREAST MASS; BREAST NEOPLASM; DATA SYSTEMS; DEPENDENT VARIABLES; DESCRIPTORS; INDEPENDENT VARIABLES; INTEROBSERVER AGREEMENT; INTEROBSERVER VARIABILITY; KAPPA STATISTIC; LOGISTIC REGRESSION ANALYSIS; LOGISTIC REGRESSIONS; RECEIVER OPERATING CHARACTERISTICS; TEST SETS;

LOGISTICS; NEURAL NETWORKS; OPTIMIZATION; REGRESSION ANALYSIS; ULTRASONOGRAPHY;

MEDICAL IMAGING;

ADULT; AGED; ARTICLE; ARTIFICIAL NEURAL NETWORK; BREAST TUMOR; COMPARATIVE STUDY; COMPUTER ASSISTED DIAGNOSIS; CONTROLLED CLINICAL TRIAL; CONTROLLED STUDY; DATA BASE; ECHOGRAPHY; ECHOMAMMOGRAPHY; FACTUAL DATABASE; FEMALE; HUMAN; METHODOLOGY; MIDDLE AGED; OBSERVER VARIATION; PATHOLOGY; RANDOMIZED CONTROLLED TRIAL; RECEIVER OPERATING CHARACTERISTIC; RETROSPECTIVE STUDY; SOUTH KOREA; STATISTICAL MODEL;

ADULT; AGED; BREAST NEOPLASMS; DATABASE MANAGEMENT SYSTEMS; DATABASES, FACTUAL; FEMALE; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; LOGISTIC MODELS; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); OBSERVER VARIATION; REPUBLIC OF KOREA; RETROSPECTIVE STUDIES; ROC CURVE; ULTRASONOGRAPHY, MAMMARY; YOUNG ADULT;

EID: 84870043235     PISSN: 08971889     EISSN: 1618727X     Source Type: Journal    
DOI: 10.1007/s10278-012-9457-7     Document Type: Article

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