Classifying Digital Mammogram Masses Using Univariate ANOVA Discriminant Analysis

Author

Surendiran, B. ; Sundaraiah, Y. ; Vadivel, A.

Author_Institution

Dept. of Comput. Applic., Nat. Inst. of Technol., Tiruchirappalli, India

fYear

2009

fDate

27-28 Oct. 2009

Firstpage

175

Lastpage

177

Abstract

An Univariate Analysis Of Variance (ANOVA) Discriminant Analysis (DA) classifier is proposed for classifying the masses present in mammogram. This approach combines the 19 shape properties of the mass regions and classifies the masses as benign or malignant using Univariate ANOVA. The experiment is performed on DDSM database images. Experimental results shows that the proposed method reaches high classification accuracy in compared to existing algorithms.

Keywords

mammography; medical diagnostic computing; pattern classification; statistical analysis; digital mammogram; discriminant analysis classifier; univariate ANOVA discriminant analysis; univariate analysis of variance; Analysis of variance; Benign tumors; Breast cancer; Cancer detection; Delta-sigma modulation; Feature extraction; Histograms; Mammography; Neural networks; Shape; Classifying as Benign or Malignant; Digital Mammogram; Discriminant analysis; Shape properties; Univariate ANOVA;

fLanguage

English

Publisher

ieee

Conference_Titel

Advances in Recent Technologies in Communication and Computing, 2009. ARTCom '09. International Conference on

Conference_Location

Kottayam, Kerala

Print_ISBN

978-1-4244-5104-3

Electronic_ISBN

978-0-7695-3845-7

Type

conf

DOI

10.1109/ARTCom.2009.33

Filename

5327893