The research on CPA diagnosis application basing on some Bayesian classifiers

Author

Bei Hui ; Lin Ji

Author_Institution

Sch. of Comput. Sci. & Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu, China

fYear

2013

fDate

17-19 Dec. 2013

Firstpage

69

Lastpage

72

Abstract

Cerebellopontine angle(CPA) masses comprise about 8-10% of all intracranial neoplasm. Preoperative diagnosis of a CPA region mass is mainly based on imaging. MRI is the best method for diagnosing the CPAM. But MRI can´t diagnose the different masses accurately. Many computer aided diagnose (CAD) technologies were developed to help radiologists to improve the diagnostic performance. The medical cases in the experiment were from West China Hospital. The experiment validates efficiency and effective of the some kinds Bayesian classifiers by 0-1 Error and RMSE. The result of experiment shows that Bayesian Classification model can classify CPAM effectively.

Keywords

belief networks; diseases; mean square error methods; medical diagnostic computing; pattern classification; 0-1 error; Bayesian classification model; CAD technologies; CPA diagnosis application; CPA region mass; CPAM classification; MRI; RMSE; West China Hospital; cerebellopontine angle; computer aided diagnosis; intracranial neoplasm; magnetic resonance imaging; root mean square error; Bayes methods; Error analysis; Hospitals; Magnetic resonance imaging; Niobium; Shape; Tumors; Cerebellopontine angle mass; MRI; semi-naive Bayes;

fLanguage

English

Publisher

ieee

Conference_Titel

Wavelet Active Media Technology and Information Processing (ICCWAMTIP), 2013 10th International Computer Conference on

Conference_Location

Chengdu

Print_ISBN

978-1-4799-2445-5

Type

conf

DOI

10.1109/ICCWAMTIP.2013.6716602

Filename

6716602