NDLI: Knowledge Expression and Inference Based on Fuzzy Bayesian Networks to Predict Astrocytoma Malignant Degree

Content Provider	IEEE Xplore Digital Library
Author	Chun-Yi Lin Jun-Xun Yin Li-Hong Ma Jian-Yu Chen Kui-Jian Wang
Copyright Year	2006
Description	Author affiliation: Coll. of Electron. & Inf. Eng., South China Univ. of Technol., Guangzhou (Chun-Yi Lin; Jun-Xun Yin; Li-Hong Ma)
Abstract	A modified fuzzy Bayesian network (FBN) is proposed in this study, which integrates fuzzy theory into Bayesian networks (BN) by using Gaussian mixture models (GMM) to make a fuzzy procedure. This particular procedure transforms continuous variables into discrete ones, when dealing with continuous inputs with probabilistic and uncertain nature. Based on the FBN, the fuzzy reasoning model for prediction and diagnosis can be designed. To validate our method, two models are built and used to classify the astrocytoma malignant degree, which can be modeled by probability quantitatively. The experiment results show that the model fusing both low-level image features and high-level semantics outperforms the one only using low-level image features with very promising results. This FBN model also provides knowledge expression in predicting astrocytoma malignant level. This study provides a novel objective method to quantitatively assess the astrocytoma malignancy level that can be used to assist doctors to diagnose the tumor
Sponsorship	IEEE Syst., Man and Cybernetics Hebei Univ.
Starting Page	3207
Ending Page	3212
File Size	256871
Page Count	6
File Format	PDF
ISBN	1424400619
DOI	10.1109/ICMLC.2006.258427
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy neural networks Bayesian methods Cancer Predictive models Neoplasms Sun Machine learning Magnetic resonance imaging Medical expert systems Uncertainty machine learning Fuzzy Bayesian networks astrocytoma diagnosis model
Content Type	Text
Resource Type	Article

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