Design of a self-adaptive brain tumor diagnostic system

ASIAIR > College of Computer Science > Department of Applied Informatics and Multimedia > Journal Article > Item 310904400/4690

Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/4690

Title:	Design of a self-adaptive brain tumor diagnostic system
Authors:	C. H. Wang;S. S. Tseng;T. P. Hong
Contributors:	Department of Information Science and Applications
Date:	1995
Issue Date:	2009-11-30 08:03:08 (UTC+0)
Publisher:	Asia University
Abstract:	This paper presents a self-adaptive expert system for brain tumor diagnosis we have designed. The Brain Tumor Diagnostic System (BTDS) we propose consists of three main components: knowledge building, inference, and knowledge refinement. In the knowledge building component, an inductive learning algorithm, RASSISTANT, constructs an initial knowledge base from noisy examples, eliminating a major difficulty in developing diagnostic expert systems. In the inference component, an inference engine exploits rules in the knowledge base to help diagnosticians determine brain tumor causes according to computer tomography pictures. A simple rule refinement scheme, PCC, is also proposed to modify the existing knowledge base during inference, which dramatically improves the accuracy of the derived rules. BTDS performance has been evaluated on 270 actual brain tumor cases. Results show that BTDS can achieve an accuracy of over 98%.
Relation:	Journal of Information Science and Engineering 11(2):274-294
Appears in Collections:	[Department of Applied Informatics and Multimedia] Journal Article

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	421	View/Open