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Reseach Article

ANN based Classifier System for Digital Mammographic Images

by Bikesh Kumar Singh, Dr. Anamika Yadav, Shailaja Singh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 35 - Number 13
Year of Publication: 2011
Authors: Bikesh Kumar Singh, Dr. Anamika Yadav, Shailaja Singh
10.5120/4564-6432

Bikesh Kumar Singh, Dr. Anamika Yadav, Shailaja Singh . ANN based Classifier System for Digital Mammographic Images. International Journal of Computer Applications. 35, 13 ( December 2011), 39-42. DOI=10.5120/4564-6432

@article{ 10.5120/4564-6432,
author = { Bikesh Kumar Singh, Dr. Anamika Yadav, Shailaja Singh },
title = { ANN based Classifier System for Digital Mammographic Images },
journal = { International Journal of Computer Applications },
issue_date = { December 2011 },
volume = { 35 },
number = { 13 },
month = { December },
year = { 2011 },
issn = { 0975-8887 },
pages = { 39-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume35/number13/4564-6432/ },
doi = { 10.5120/4564-6432 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:21:53.622587+05:30
%A Bikesh Kumar Singh
%A Dr. Anamika Yadav
%A Shailaja Singh
%T ANN based Classifier System for Digital Mammographic Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 35
%N 13
%P 39-42
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Breast cancer is one of the most common cancers among women of the developing countries in the world, and it has also become a major cause of death [1, 2]. Treatment of breast cancer is effective only if it is detected at an early stage. X-ray Mammography is the most effective technique used by radiologists in the screening and diagnosis of breast cancer in women but the mammographic images are complex [2]. With the development in Artificial Intelligence (AI) and Soft Computing Techniques, Computer-Aided Diagnosis (CAD) attracts more and more attention for brain tumor diagnosis. Computer-Aided Diagnosis system (CAD) can be very helpful in detecting and diagnosing breast abnormalities earlier and faster than typical screening programs. This paper presents retrieval and ANN (Artificial neural network) based classification system for computer aided diagnosis of breast cancer using texture features. The proposed system uses Euclidean distance for the comparison of the feature vector of the query image and each image in the database. It has been found that the proposed CBIR system is gives 80% retrieval accuracy for the database of 200 images of mini-MIAS database. Further the ANN based classifier gives 94% accuracy in classifying benign and malignant breast masses. MATLAB ® 7.01 image processing toolbox and ANN toolbox have been used to implement the algorithm. The results show that texture features can be effectively used for classifying mammographic images with high level of accuracy.

References
  1. J. Ferlay, F. Bray, P. Pisani and D.M. Parkin. GLOBOCAN 2000: Cancer Incidence, Mortality and Prevalence Worldwide. Version 1.0. IARC Cancer Base No. 5. Lyon, IARC Press, 2001.
  2. Hala Al-Shamlan, Ali El-Zaart,” Feature Extraction Values for Breast Cancer Mammography Images”, proceedings of International conference on Bioinformatics and Biomedical Technology (ICBBT), pp. 335 – 340, 2010.
  3. B.K.Singh,” Mammographic Image Enhancement, Classification and Retrieval Using Color, Statistical and Spectral Analysis, IJCA, 27(1):pp. 18-23, August 2011.
  4. J Suckling et al (1994): The Mammographic Image Analysis Society Digital Mammogram Database Exerpta Medica. International Congress Series 1069 pp375-378.
  5. Tabar L., Dean B.,” Teaching Atlas of Mammography”, 2nd edition, Thieme Newyork (1985).
  6. American Cancer Society, “Cancer Facts and Figures 2003”, Atlanta, GA: American Cancer Society, 2003
  7. K.Rajakumar, Dr.S.Muttan ,”Medical Image Retrieval using Energy Efficient Wavelet Transform”, Second International conference on Computing, Communication and Networking Technologies, Chennai,.India (2010).
  8. Y. Liu, D.S. Zhang, G. Lu and W.-Y. Ma, “Region-based image retrieval with perceptual colors, Proceedings of the Pacific-Rim Multimedia Conference (PCM),” December 2004, pp. 931–938.
  9. R. Shi, H. Feng, T.-S. Chua and C.-H. Lee, “An adaptive image content representation and segmentation approach to automatic image annotation,” International Conference on Image and Video Retrieval (CIVR), 2004, pp. 545–554.
  10. V. Mezaris, I. Kompatsiaris and M.G. Strintzis, “ontology approach to object-based image retrieval,” Proceedings of the ICIP, vol. II, 2003, pp. 511–514.
  11. Y. Ireaneus Anna Rejani, “ Early Detection of Breast Cancer using SVM Classifier Technique”, International Journal of Computer Science and Engineering Vol.1(3), pp. 127-130, 2009.
  12. B. K. Singh, G. R. Sinha, A. Wany and B. Mazumdar, Content based retrieval from biomedical images using color histograms, proceedings of International Conference on Nanotechnology and Biosensors, ICBN , Raghu College of Engg., Visakhapatnam , India 2010.
  13. R. M. Haralick, K. Shanmugam, and I. H. Dinstein, "Textural Features for Image Classification," IEEE Transactions on Systems, Man and Cybernetics, vol. 3, pp. 610-621.
  14. R. M. Haralick, "Statistical and structural approaches to texture," Proceedings of the IEEE, vol. 67, pp. 786-804, 1979.
  15. Scott Lindell, Gray Shapiro, Keneneth Weil, David Flannery, Jeffrey Levy, Wei Qian, “ Development of Mammogram Computer – Aided Diagnosis Systems Using Optical Processing Technology”, proceedings of workshop on Applied Imagery Pattern Recognition pp 173 - 179 , 2000.
  16. Tulio C.S.S. Andre and Rangaraj M. Rangayyan, “ Classification of Tumors and Masses in Mammograms Using Neural Networks with Shape and Texture Features”, Proceedings of 25th Annual International Conference on, pp. 2261 - 2264 Vol.3, 2003.
Index Terms

Computer Science
Information Sciences

Keywords

Mammograms image processing shape and texture features Content Bases Image Retrieval (CBIR) ANN (Artificial Neural Network)