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Meharunnisa, S. P.
- Improvement in Detection Accuracy of Digital Mammogram Using Point Transform and Data Mining Technique
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Authors
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1 Department of Electronics and Instrumentation Engineering, Visvesvaraya Technological University, IN
1 Department of Electronics and Instrumentation Engineering, Visvesvaraya Technological University, IN
Source
ICTACT Journal on Image and Video Processing, Vol 9, No 1 (2018), Pagination: 1838-1843Abstract
Cancer is one of the dangerous diseases faced by humans. Every one out of 100 women is facing breast cancer. So, to overcome this huge ratio many researches are being carried out. Prevention is better than cure; this paper presents one such attempt of detecting breast cancer in the early stages. In proposed method exponential point transform is carried out for image enhancement and in preprocessing stage pectoral mass is removed from the mammogram image. As the next step we apply K-means algorithm and morphological processing to identify the infected region and removal of unwanted region. Finally, Decision Tree Data mining technique is used for classifying features to detect presence of tumor. Hence by this approach we get more accurate results. The experimental results gave an accuracy of 97.03 %.Keywords
Breast Cancer, Point Transform, K-means, Decision Tree Classifier.
References
- Suhas Sapate and Sanjay Talbar, “An Overview of Pectoral Muscle Extraction Algorithms applies to Digital Mammograms”, Medical Imaging in Clinical Applications, pp. 19-54, 2016.
- Mussarat Yasmin, Muhammad Sharif and Sajjad Mohsin, “Survey Paper on Diagnosis of Breast Cancer using Image Processing Techniques”, Recent Journal of Recent Sciences, Vol. 2, No. 10, pp. 88-98, 2013.
- K. Ganesan et al., “Computer-Aided Breast Cancer Detection using Mammograms: A Review”, IEEE Reviews in Biomedical Engineering, Vol. 6, pp. 77-98, 2012.
- K. Hu, X. Gao and F. Li, “Detection of Suspicious Lesions by Adaptive Thresholding based on Multiresolution Analysis in Mammograms”, IEEE Transactions on Instrumentation and Measurement, Vol. 60, No. 2, pp. 462-472, 2011.
- B.N. Prathibha and V. Sadasivam, “Mammogram Analysis using SVM Classifier in combined Transforms Domain”, ICTACT Journal on Image and Video Processing, Vol. 01, No. 3, pp. 172-177, 2011.
- J Anitha, J Dinesh Peter and Immanuel Alex Pandian, “A Dual Stage Adaptive Thresholding (DuSAT) for Automatic Mass Detection in Mammograms”, Computer Methods and Medicine in Mammograms, Vol. 138, pp. 93-104, 2017.
- Aswini Kumar Mohanty, Manas Ranjan Senapathy, Swapnasikta Baberta and Saroj Kumar Lenka, “Texture based Features for Classification of Mammograms using Decision Tree”, Neural Computing and Applications, Vol. 23, No. 3-4, pp. 1011-1017, 2013.
- R.M. Haralick, K. Shanmugam and I.H. Dinstein, “Texture Features for Image Classification”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 3, No. 6, pp. 610-621, 1973.
- E. Walid and H. Hakim, “A New Cost Sensitive Decision Tree Method Application for Mammograms Classification”, International Journal of Computer Science and Network Security, Vol. 6, No. 11, pp. 130-138, 2006.
- K. Polat and S. Gunes, “A Novel Hybrid Intelligent Method based on C4.5 Decision Tree Classifier and One-Against-all Approach for Multi-Class Classification Problems”, Expert Systems with Applications, Vol. 36, No. 2, pp. 1587-1592, 2009.
- M. Hall-Beye, “GLCM Texture: A Tutorial v.2.7.1”, Available at: www.fp.ucalgary.ca/mhallbey/tutorial.htm, Accessed on 2004.
- Samir Kumar Bandypodhyay, “Formation of Homogeneous blocks for Segmentation of Mammograms”, International Journal of Engineering Science and Technology, Vol. 2, No. 12, pp. 7444-7448, 2010.
- Yufeng Zheng, “Breast Cancer Detection with Gabor Features from Digital Mammograms”, Algorithms, Vol. 3, No. 1, pp. 44-62, 2010.
- J. Abdul Jaleel, S. Salim and S. Archana, “Mammogram Mass Classification Based on Discrete Wavelet Transform Textural Features”, Proceedings of International Conference on Advances in Computing, Communications and Informatics, pp. 24-27, 2014.
- Washington W. Azevedo et al., “Fuzzy Morphological Extreme Learning Machines to Detect and Classify Masses in Mammograms”, Proceedings of IEEE International conference on Fuzzy Systems, pp. 1-6, 2015.
- M.E. Elamnna and Y.M. Kadah, “Implementation of Practical Computer Aided Diagnosis System for Classification of Masses in Digital Mammograms”, Proceedings of International Conference on Computing, Control, Networking, Electronics & Embedded Systems Engineering, pp. 7-13, 2015.
- Shen-Chuan Tai, Zih-Siou Chen and Wei-Ting Tsai, “An Automatic Mass Detection System in Mammograms Based on Complex Texture Features”, IEEE Journal of Biomedical and Health Informatics, Vol. 18, No. 2, pp. 618-627, 2014.
- Uma Ojha and Savitha Goel, “A Study on Prediction of Breast Cancer Recurrence using Data Mining Techniques”, Proceedings of 7th International Conference on Cloud Computing, Data Science Engineering, pp. 107-113, 2017.
- B. Shradhananda, B. Majhi and R. Dash, “Mammogram Classification using Two Dimensional Discrete Wavelet Transform and Grey Level Co-occurrence Matrix for Detection of Breast Cancer”, Neurocomputing, Vol. 154, pp. 1-14, 2014.
- Zhe Jiang, Shashi Shekhar, Xen Zhou, Joseph Knight and Jennifer Corcoran, “Focal-Test Based Spatial Decision Tree Learning”, IEEE Transactions on Knowledge and Data Engineering, Vol. 27, No. 6, pp. 1547-1559, 2015.
- Ibrahim Mohamed Jaber Alamin, W. Jeberson and H K Bajaj “Improved Framework for Breast Cancer Detection using Hybrid Feature Extraction and FFNN”, International Journal of Advanced Research in Artificial Intelligence, Vol. 5, No. 8, pp. 1-6, 2016.
- Rodrigo Coelho Barros, Marcio Porto Basgalupp, Andre C.P.L.F. De Carvalho and Alex A. Freitas, “A Survey of Evolutionary Algorithms for Decision Tree Induction”, IEEE Transactions on Systems, Man, Cybernatics-Part C: Applications and Reviews, Vol. 42, No. 3, pp. 112-132, 2012.
- Jinshan Tang, Rangaraj M. Rangayyan, Issam EI Naqa and Yongyi Yang, “Computer-Aided Detection and Diagnosis of Breast Cancer with Mammography: Recent Advances”, IEEE Transactions on Information Technology in Biomedicine, Vol. 13, No. 2, pp. 236-251, 2009.
- S.P. Meharunnisa, K Suresh and M. Ravishankar, “Detection of Breast Masses in Digital Mammograms using SVM”, International Science Press, Vol. 8, No. 3, pp. 899-906, 2015.
- S.P. Meharunnisa and K Suresh, “Identification of Microcalcifications for Early Signs of Breast Cancer”, STM Journals, Vol. 4, No. 3, pp. 7-12, 2014.
- S.P. Meharunnisa, B. Amith and K Suresh, “Early Detection of Breast Cancer using Computer Aided Detection and Diagnosis Recent Advances”, International Journal of Engineering Research and Technology, Vol. 5, No. 3, pp. 43-51, 2015.
- S.P. Meharunnisa, B. Amith and K Suresh, “Detection and Classification of masses in Breast Cancer using Computer Aided Detection and Diagnosis”, International Journal for Research and Development in Technology, Vol. 3, No. 6, pp. 1-7, 2015.
- Xiang Zhong, Jingshan Li, Susan M. Ertl, Carol Hassemer, and Lauren Fiedler, “A System-Theoretic Approach to Modeling and Analysis of Mammography Testing Process”, IEEE Transactions On Systems, Man, and Cybernetics: Systems, Vol. 46, No. 1, pp. 126-138, 2016.