Call for Paper - March 2023 Edition
IJCA solicits original research papers for the March 2023 Edition. Last date of manuscript submission is February 20, 2023. Read More

Classification of Microcalcification in Digital Mammogram using Stochastic Neighbor Embedding and KNN Classifier

Print
PDF
IJCA Proceedings on Emerging Technology Trends on Advanced Engineering Research - 2012
© 2013 by IJCA Journal
ICETT - Number 1
Year of Publication: 2013
Authors:
S. Mohan Kumar
G. Balakrishnan

Mohan S Kumar and G Balakrishnan. Article: Classification of Microcalcification in Digital Mammogram using Stochastic Neighbor Embedding and KNN Classifier. IJCA Proceedings on Emerging Technology Trends on Advanced Engineering Research - 2012 ICETT(1):5-9, January 2013. Full text available. BibTeX

@article{key:article,
	author = {S. Mohan Kumar and G. Balakrishnan},
	title = {Article: Classification of Microcalcification in Digital Mammogram using Stochastic Neighbor Embedding and KNN Classifier},
	journal = {IJCA Proceedings on Emerging Technology Trends on Advanced Engineering Research - 2012},
	year = {2013},
	volume = {ICETT},
	number = {1},
	pages = {5-9},
	month = {January},
	note = {Full text available}
}

Abstract

Breast cancer has become a common health problem in developed and developing countries during the last decades and also the leading cause of mortality in women each year. Mammogram is a special x-ray examination of the breast made with specific x-ray equipment that can often find tumors too small to be felt. In this paper, the classification of microcalcification in digital mammogram is achieved by using Stochastic Neighbor Embedding (SNE) for reducing high dimensionality data into relatively low dimensional data and K-Nearest Neighbor (KNN) Classifier. This system classifies the mammogram images into normal or abnormal, and the abnormal severity into benign or malignant. Mammography Image Analysis society (MIAS) database is used to evaluate the proposed system. The experiments demonstrate that the proposed method can provide better classification rate.

References

  • Songyang Yu and Ling Guan, "A CAD System for the Automatic Detection of Clustered Microcalcifications in Digitized Mammogram Films", IEEE Transactions on Medical imaging, vol. 19, no. 2, February 2000, pp 115-126.
  • Ryohei Nakayama and Yoshikazu Uchiyama, "Computer-Aided Diagnosis Scheme Using a Filter Bank for Detection of Microcalcification Clusters in Mammograms", IEEE Transactions on Biomedical engineering, vol. 53, no. 2, February 2006, pp 273-283.
  • M. Suganthi and M. Madheswaran, "Mammogram Tumor Classification using Multimodal Features and Genetic Algorithm", IEEE International Conference on "Control, Automation, Communication and Energy conservation, June 2009, pp 1-6.
  • Ibrahima Faye and Brahim Belhaouari Samir, "Digital Mammograms Classification Using a Wavelet Based Feature Extraction Method", IEEE conference on Computer and Electrical Engineering, 2009, pp 318-322.
  • Peter Mc Leod and Brijesh Verma, "A Classifier with Clustered Sub Classes for the Classification of Suspicious Areas in Digital Mammograms", IEEE conference on Neural Networks, July 2010, pp 1-8.
  • Viet Dzung Nguyen, Thu Van Nguyen and Tien Dzung Nguyen, "Detect Abnormalities in Mammograms by Local Contrast Thresholding and Rule-based Classification", IEEE third International Conference on Communications and Electronics, August 2010, pp 207-210.
  • Andy Tirtajaya and Diaz D. Santika, "Classification of Microcalcification Using Dual-Tree Complex Wavelet Transform and Support Vector Machine", IEEE International Conference on Advances in Computing, Control and Telecommunication Technologies, December 2010, pp 164-166.
  • Fatemeh Saki and Amir Tahmasbi, "A Novel Opposition-based Classifier for Mass Diagnosis in Mammography Images", IEEE Iranian Conference of Biomedical Engineering, November 2010, pp 1-4.
  • Alireza Shirazi Noodeh and Hossein Rabbani, "Detection of Cancerous Zones in Mammograms using Fractal Modeling and Classification by Probabilistic Neural Network" IEEE Iranian Conference of Biomedical Engineering, November 2010, pp 1-4.
  • K. Thangavel and A. Kaja Mohideen, "Semi-Supervised K-Means Clustering for Outlier Detection in Mammogram Classification", IEEE Trendz in Information Sciences & Computing, December 2010, pp 68-72.
  • Mohamed Meselhy Eltoukhy and Ibrahima Faye, "Curvelet Based Feature Extraction Method for Breast Cancer Diagnosis in Digital Mammogram", IEEE International Conference on Intelligent and Advanced Systems, June 2010, pp 1-5.
  • Dheeba. J and Tamil Selvi. S, "Classification of Malignant and Benign Microcalcification Using SVM Classifier", IEEE International Conference on Emerging Trends in Electrical and Computer Technology, March 2011, pp 686-690.