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KBANNS and the Classification of 31 P MRS of Malignant Mammary Tissues
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posted on 2023-06-08, 07:26 authored by Margarita Sordo, Hilary Buxton, Des WatsonKnowledge-based artificial neural networks (KBANNs) is a hybrid methodology that combines knowledge of a domain in the form of simple rules with connectionist learning. This combination allows the use of small sets of data (typical of medical diagnosis tasks) to train the network. The initial structure is set from the dependencies of a set of rules and it is only necessary to refine these rules by training. In this paper we present such KBANNs with a topology derived from knowledge elicited from the domain of metabolic features of malignant mammary tissues. KBANN performance is assessed over the classification of 26 in vivo P-31 spectra of normal and cancerous breast tissues. Results presented in this paper confirm the suitability of KBANNs a computational aid capable of classifying complex and limited data in a medical domain. The present study is part of an ongoing investigation into normal and abnormal breast physiology which may allow non-invasive early detection of breast cancer [27,28].
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Publication status
- Published
ISSN
0537-9989Publisher
INST ELECTRICAL ENGINEERS INSPEC INC, 379 THORNALL ST, EDISON, NJ 08837 USAIssue
470Volume
IIPage range
982-987Presentation Type
- paper
Event name
9th International Conference on Artificial Neural Networks (ICANN99)Event location
UNIV EDINBURGH, EDINBURGH, SCOTLAND, SEP 07-10, 1999Event type
conferenceISBN
0-85296-721-7Department affiliated with
- Informatics Publications
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- No
Peer reviewed?
- Yes
Legacy Posted Date
2012-02-06Usage metrics
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