Qualitative Study on Data Mining Algorithms for Classification of Mammogram Images

N. Arivazhagan and S. Govindarajan ()
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N. Arivazhagan: SRM University, School of Computing
S. Govindarajan: SRM University, School of Public Health

A chapter in New Trends in Computational Vision and Bio-inspired Computing, 2020, pp 483-489 from Springer

Abstract: Abstract Automatic detection of cancer by digital images using data mining algorithm is required for next generation medical data analysis system. Classification of mammogram images for cancer detection is carried out with different machine learning algorithms as SVM (Support vector machine), KNN, Naive Bayesian. Analysing these algorithms are carried out to identify a technique that can provide the best possible result for all variant of images. This paper presents a qualitative study on these algorithms. The result of the analysis declares that SVM with bagging provides the better performance in the classification of mammogram images with accuracy of 99.467%.

Keywords: Mammogram images; Image classification; Feature extraction; Data mining algorithms (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-41862-5_46

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DOI: 10.1007/978-3-030-41862-5_46

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