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Methods Inf Med 2007; 46(02): 212-215
DOI: 10.1055/s-0038-1625409
Original Article
Schattauer GmbH

Independent Component Analysis and Neural Networks Applied for Classification of Malignant, Benign and Normal Tissue in Digital Mammography

L. F. A. Campos
1   Laboratory for Biological Information Processing, University Federal of Maranhão, São Luis, Brazil
,
A. C. Silva
1   Laboratory for Biological Information Processing, University Federal of Maranhão, São Luis, Brazil
,
A. K. Barros
1   Laboratory for Biological Information Processing, University Federal of Maranhão, São Luis, Brazil
› Author Affiliations
Further Information

Publication History

Publication Date:
11 January 2018 (online)

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Summary

Objectives : This paper proposes an efficient method for the discrimination and classification of mammograms with benign, malignant and normal tissues.

Methods : The proposed method consists of selection of tissues, feature extraction using independent component analysis, feature selection by the foiward- selection technique and classification of the tissue by the multilayer perceptron.

Results : The method is tested for a mammogram set of the MIAS database, resulting in a 97.83% success rate, with 98.0% specificity and 97.5% sensitivity.

Conclusion : The proposed method showed a good classification rate. The method will be useful for early cancer diagnosis.

Keywords

Mammogram - breast cancer - independent component analysis - computer-aided diagnosis - multilayer perceptron neural networks
 

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