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Journals / Istanbul University Journal of Electrical and Electronics Engineering / 2009 / Cilt: 9 - Sayı: 1

Mammographic mass classification using wavelet based support vector machine

Pelin GORGEL
Ahmet SERTBAŞ
Niyazi KILIC
Osman N. UCAN
Onur OSMAN
Pages
867–875
Publication Date
2022-10-18
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Also confusion matrix, accuracy, sensitivity andspecificity analysis with different kernel types were used to show the classification performance ofSVM.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this paper, we investigate an approach for classification of mammographic masses as benign ormalign.
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Key Findings & Data:The research involved 66 digitized mammographic images.The masses were segmented manually by radiologists, prior to introduction to the classificationsystem.
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Conclusion & Impact:Also confusion matrix, accuracy, sensitivity andspecificity analysis with different kernel types were used to show the classification performance ofSVM.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

In this paper, we investigate an approach for classification of mammographic masses as benign ormalign. This study relies on a combination of Support Vector Machine (SVM) and wavelet-basedsubband image decomposition. Decision making was performed in two stages as feature extraction bycomputing the wavelet coefficients and classification using the classifier trained on the extractedfeatures. SVM, a learning machine based on statistical learning theory, was trained throughsupervised learning to classify masses. The research involved 66 digitized mammographic images.The masses were segmented manually by radiologists, prior to introduction to the classificationsystem. Preliminary test on mammogram showed over 84.8% classification accuracy by using theSVM with Radial Basis Function (RBF) kernel. Also confusion matrix, accuracy, sensitivity andspecificity analysis with different kernel types were used to show the classification performance ofSVM.

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Cite This Article

GORGEL, P., SERTBAŞ, A., KILIC, N., UCAN, O. N., OSMAN, O. (2022). Mammographic mass classification using wavelet based support vector machine. Istanbul University Journal of Electrical and Electronics Engineering, Cilt: 9 - Sayı: 1, 867–875. https://ajindex.com/articles/543756

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