research-article

Breast cancer classification of mammographic masses using improved shape features

Authors:

Hyung D. ChoiAuthors Info & Claims

RACS '15: Proceedings of the 2015 Conference on research in adaptive and convergent systems

Pages 188 - 194

https://doi.org/10.1145/2811411.2811507

Published: 09 October 2015 Publication History

Abstract

Breast cancer classification technique divides breast cancer into two categories, benign tumors and malignant tumors. The main purpose of breast cancer classification is to classify abnormalities into benign or malignant classes and thus help physicians with further analysis by minimizing the possible errors that can be done because of fatigued or inexperienced physician. In this paper, we propose three new shape features to classify mammographic images into benign and malignant class. SVM is used as a machine learning tool for training and classification purpose. In order to evaluate the improved performance of the proposed shape features, convexity, circularity and a modified global shape feature of compactness was used. The result shows that the proposed shape features can improve measure of performances such as MCC, specificity, sensitivity and accuracy and can be a promising tool to provide preliminary decision support information to physicians for further diagnosis.

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Cited By

Saikia SV. VPrajapati BPrajapati JParihar AMalviya R(2023)Computational Tools for Drug Discovery of Anticancer TherapyTargeted Cancer Therapy in Biomedical Engineering10.1007/978-981-19-9786-0_25(887-904)Online publication date: 12-Apr-2023
https://doi.org/10.1007/978-981-19-9786-0_25
Mishra ARoy PBandyopadhyay SDas S(2022)Achieving highly efficient breast ultrasound tumor classification with deep convolutional neural networksInternational Journal of Information Technology10.1007/s41870-022-00901-414:7(3311-3320)Online publication date: 19-Feb-2022
https://doi.org/10.1007/s41870-022-00901-4
Rehman KLi JPei YYasin A(2022)A review on machine learning techniques for the assessment of image grading in breast mammogramInternational Journal of Machine Learning and Cybernetics10.1007/s13042-022-01546-213:9(2609-2635)Online publication date: 1-Apr-2022
https://doi.org/10.1007/s13042-022-01546-2

Index Terms

Breast cancer classification of mammographic masses using improved shape features

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Published In

cover image ACM Conferences

RACS '15: Proceedings of the 2015 Conference on research in adaptive and convergent systems

October 2015

540 pages

ISBN:9781450337380

DOI:10.1145/2811411

Conference Chairs:
Esmaeil S. Nadimi
University of Southern Denmark, Denmark
,
Tomas Cerny
Czech Technical University, Czech Republic
,
Program Chairs:
Sung-Ryul Kim
Konkuk University, Korea
,
Wei Wang
San Diego State University

Copyright © 2015 ACM.

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Published: 09 October 2015

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RACS '15: International Conference on Research in Adaptive and Convergent Systems

October 9 - 12, 2015

Prague, Czech Republic

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RACS '15 Paper Acceptance Rate 75 of 309 submissions, 24%;

Overall Acceptance Rate 393 of 1,581 submissions, 25%

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Cited By

Saikia SV. VPrajapati BPrajapati JParihar AMalviya R(2023)Computational Tools for Drug Discovery of Anticancer TherapyTargeted Cancer Therapy in Biomedical Engineering10.1007/978-981-19-9786-0_25(887-904)Online publication date: 12-Apr-2023
https://doi.org/10.1007/978-981-19-9786-0_25
Mishra ARoy PBandyopadhyay SDas S(2022)Achieving highly efficient breast ultrasound tumor classification with deep convolutional neural networksInternational Journal of Information Technology10.1007/s41870-022-00901-414:7(3311-3320)Online publication date: 19-Feb-2022
https://doi.org/10.1007/s41870-022-00901-4
Rehman KLi JPei YYasin A(2022)A review on machine learning techniques for the assessment of image grading in breast mammogramInternational Journal of Machine Learning and Cybernetics10.1007/s13042-022-01546-213:9(2609-2635)Online publication date: 1-Apr-2022
https://doi.org/10.1007/s13042-022-01546-2

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