KBANNs and the Classification of ³¹P MRS of Malignant Mammary Tissues

Abstract

Knowledge-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. KBANNs' performance is assessed over the classification of 26 in vivo ³¹P 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 [29, 30]