DocumentCode :
1933197
Title :
A breast cancer classifier based on a combination of case-based reasoning and ontology approach
Author :
Lotfy Abdrabou, Essam Amin M ; Salem, AbdEl-Badeeh M.
Author_Institution :
Fac. of Comput. & Inf. Sci., Ain Shams Univ., Cairo, Egypt
fYear :
2010
fDate :
18-20 Oct. 2010
Firstpage :
3
Lastpage :
10
Abstract :
Breast cancer is the second most common form of cancer amongst females and also the fifth most cause of cancer deaths worldwide. In case of this particular type of malignancy, early detection is the best form of cure and hence timely and accurate diagnosis of the tumor is extremely vital. Extensive research has been carried out on automating the critical diagnosis procedure as various machine learning algorithms have been developed to aid physicians in optimizing the decision task effectively. In this research, we present a benign/malignant breast cancer classification model based on a combination of ontology and case-based reasoning to effectively classify breast cancer tumors as either malignant or benign. This classification system makes use of clinical data. Two CBR object-oriented frameworks based on ontology are used jCOLIBRI and myCBR. A breast cancer diagnostic prototype is built. During prototyping, we examine the use and functionality of the two focused frameworks.
Keywords :
cancer; case-based reasoning; learning (artificial intelligence); medical image processing; ontologies (artificial intelligence); patient diagnosis; tumours; benign tumors; breast cancer; cancer classification; cancer diagnosis; case-based reasoning; clinical data; machine learning; malignant tumors; ontology; Breast cancer; Buildings; Cognition; Graphical user interfaces; Libraries; Ontologies; Breast Cancer; CBR; CBR Frameworks; Case-Based Reasoning; Case-Based Reasoning Frameworks; jCOLIBRI; myCBR;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Science and Information Technology (IMCSIT), Proceedings of the 2010 International Multiconference on
Conference_Location :
Wisla
ISSN :
2157-5525
Print_ISBN :
978-1-4244-6432-6
Type :
conf
DOI :
10.1109/IMCSIT.2010.5680045
Filename :
5680045
Link To Document :
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