Open AccessComputer Aided Diagnosis of Clustered Microcalcifications Using Artificial Neural Nets
Author(s) -
Erich Sorantin,
Ferdinand Schmidt,
Heinz Mayer,
Michael Becker,
Csaba Szepesvári,
E. Graif,
Peter Winkler
Publication year - 2000
Publication title -
cit. journal of computing and information technology/journal of computing and information technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.169
H-Index - 27
eISSN - 1846-3908
pISSN - 1330-1136
DOI - 10.2498/cit.2000.02.06
Subject(s) - computer science , indeterminate , artificial neural network , artificial intelligence , pattern recognition (psychology) , identification (biology) , computer aided diagnosis , cluster (spacecraft) , mammography , microcalcification , medicine , mathematics , botany , cancer , breast cancer , pure mathematics , biology , programming language
Objective: Development of a fully automated computer application for detection and classification of clustered microcalcifications using neural nets. Material and Methods: Mammographic films with clustered microcalcifications of known histology were digitized. All clusters were rated by two radiologists on a 3 point scale: benign, indeterminate and malignant. Automated detected clustered microcalcifications were clustered. Features derived from those clusters were used as input to 2 artificial neural nets: one was trained to identify the indeterminate clusters, whereas the second ANN classified the remaining clusters in benign or malignant ones. Performance evaluation followed the patient-based receiver operator characteristic analysis. Results: For identification of patients with indeterminate clusters a an Az-value of 0.8741 could be achieved. For the remaining patients their clusters could be classified as benign or malignant at an Az-value of 0.8749, a sensitivity of 0.977 and specificity of 0.471. Conclusions: A fully automated computer system for detection and classification of clustered microcalcifications was developed. The system is able to identify patients with indeterminate clusters, where additional investigations are recommended, and produces a reliable estimation of the biologic dignity for the remaining ones