Self-integrating knowledge-based brain tumor diagnostic system

Abstract

In this paper we present a self-integrating knowledge-based expert system for brain tumor diagnosis. The system we propose comprises knowledge building, knowledge inference and knowledge refinement. During knowledge building, an automatic knowledge-integration process, based on Darwin's theory of natural selection, integrates knowledge derived from knowledge-acquisition tools and machine-learning methods to construct an initial knowledge base, thus eliminating a major bottleneck in developing a brain tumor diagnostic system. During the knowledge inference process, art inference engine exploits rules in the knowledge base to help diagnosticians determine brain tumor etiologies according to computer tomography pictures. And, a simple knowledge refinement method is proposed to modify the existing knowledge base during inference, which dramatically improves the accuracy of the derived rules. The performance of the brain tumor diagnostic system has been evaluated on actual brain tumor cases. Copyright (C) 1996 Elsevier Science Ltd