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Wavelet Feature and SVM for Detection and Classification of Microcalcifications

Jhansi J*, Kalpana M**, Shobha M***

*-*** Assistant Professor, Department of Electronics and Communication Engineering, Siddharth institute of Engineering & Technology, Andhra Pradesh, India.

Periodicity:January - March'2017
DOI : https://doi.org/10.26634/jdp.5.1.13526

Abstract

The objective of this paper is to detect the microcalcifications from the digitized mammograms using support vector machine, based on effective wavelet feature analysis. Microcalcifications are tiny deposits of calcium in the breast tissue which are potential indicators for early detection of breast cancer. The identification of cancer tissue is prohibited by the poor contrast level of mammograms. In this paper, new approach helps to identify cancer tissue with better accuracy. Microcalcifications are extracted by using wavelet based feature extraction and compared with other feature extraction like Gabor filter based extraction. The results from the feature extraction are classified using support vector machine classifier that provides better performance than other classifiers on wavelet based feature extraction.

Keywords

Microcalcifications, Wavelet, Gabor Filter, Support Vector Machine.

How to Cite this Article?

Jhansi J., Kalpana M., Shobha M. (2017). Wavelet Feature and SVM for Detection and Classification of Microcalcifications. i-manager’s Journal on Digital Signal Processing, 5(1), 7-12. https://doi.org/10.26634/jdp.5.1.13526

References

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Wavelet Feature and SVM for Detection and Classification of Microcalcifications

Abstract

Keywords

How to Cite this Article?

References

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