Paper Info
Title
A breast tumor classification method based on ultrasound BI-RADS data mining
Abstract
In this paper, to reduce the response time of computer-aided diagnostic (CAD) systems, we proposed a feature selection algorithm that utilizes BI-RADS which is the standard clinical considerations for radiologists to illustrate the visual characteristics of breast tumors. We first apply the association rule mining technique to the medical database annotated with BI-RADS lexicons by doctors, to find out the interesting BI-RADS lexicon values. Then, we select the image processing algorithms which effectively represent the chosen BI-RADS lexicon values. Finally, the features obtained from the selected image processing algorithms are used to build our classifier using Support Vector Machine (SVM) to predict whether each tumor is benign or malignant. Our experimental result shows that our classifier is accurate with fast execution time.
Year
Venue
Keywords
2012
APSIPA
mammography,breast tumor visual characteristics,feature selection algorithm,benign tumor,bi-rads lexicon values,image processing algorithms,computer aided diagnostic systems,ultrasound bi-rads data mining,bi-rads lexicon annotated medical database,breast tumor classification method,biomedical ultrasonics,feature extraction,image classification,support vector machine,data mining,cad system response time,tumours,association rule mining technique,support vector machines,medical image processing,malignant tumor
Field
DocType
ISSN
Mammography,Data mining,Pattern recognition,Feature selection,Computer science,Support vector machine,Feature extraction,Association rule learning,Artificial intelligence,Contextual image classification,Digital image processing,Classifier (linguistics)
Conference
2309-9402
ISBN
Citations 
PageRank 
978-1-4673-4863-8
0
0.34
References 
Authors
3
6
Name
Order
Citations
PageRank
Jin Man Park110.70
Hyoungmin Park2964.83
Jong-Ha Lee312.07
Yeong Kyeong Seong4226.38
Kyoung-Gu Woo59710.37
Kyuseok Shim65120752.19