Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2017, Cilt: 5 Sayı: 1, 310 - 316, 30.06.2017
https://doi.org/10.17261/Pressacademia.2017.605

Öz

Kaynakça

  • V. Gaike, R. Mhaske, S. Sonawane, N. Akhter, P. D. Deshmukh, "Clustering of breast cancer tumor using third order GLCM feature", , vol. 00, no. , pp. 318-322, 2015
  • A. Sahar “Predicting the Serverity of Breast Masses with Data Mining Methods” International Journal of Computer Science Issues, Vol. 10, Issues 2, No 2, March 2013 ISSN (Print):1694-0814| ISSN (Online):1694-0784 www.IJCSI.org.
  • S. Sondele and I. Saini, "Classification of Mammograms Using Bidimensional Empirical Mode Decomposition Based Features and Artificial Neural Network", International Journal of Bio-Science and Bio-Technology, Vol.5, No.6 (2013), pp.171-180.
  • Z. K. Senturk and R. “Breast Cancer Diagnosis Via Data Mining: Performance Analysis of Seven Different Algorithms”, Computer Science & Engineering: An International Journal (CSEIJ), Vol. 4, No. 1, February 2014.
  • M. P. Sampat, M. K. Markey, and A. C. Bovik, "Computer-Aided Detection and Diagnosis in Mammography," in Handbook of Image and Video Processing, A. C. Bovik, Ed., 2nd ed, 2005.
  • Salama Gouda I., M. B. Abdelhalim, and Magdy Abd-elghany Zeid. "Experimental comparison of classifiers for breast cancer diagnosis." Computer Engineering & Systems (ICCES), 2012 Seventh International Conference on. IEEE, 2012.
  • Mittal Dishant, Dev Gaurav, and Sanjiban Sekhar Roy. "An effective hybridized classifier for breast cancer diagnosis." 2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM). IEEE, 2015.
  • M. Vasantha, S. Bharathi V and R. Dhamodharan, “Medical image feature, extraction, selection and classification”. Intl. J. Engg.Sci. & Technol. 2(6), 2071-2076, 2010.
  • S M Halawani “A study of digital mammograms by using clustering algorithms” Journal of Scientific & Industrial Research Vol. 71, September 2012, pp. 594-600.
  • B. Padmapriya and T. Velmurugan, "A survey on breast cancer analysis using data mining techniques," 2014 IEEE International Conference on Computational Intelligence and Computing Research, Coimbatore, 2014, pp. 1-4.
  • K. Kourou, T. P. Exarchos, K. P. Exarchos , M. V. Karamouzis, D. I. Fotiadis, “Machine learning applications in cancer prognosis and prediction”, Computational and Structural Biotechnology Journal 13 (2015) 8–17.
  • Cruz JA, Wishart DS. “Applications of Machine Learning in Cancer Prediction and Prognosis”. Cancer Informatics. 2006;2:59-77.
  • Mammographic Image Analysis, http://www.mammoimage.org/databases/. Last accessed: March, 2017.
  • R. Sharmila and R. Uma, “A new approach to image contrast enhancement using weighted threshold histogram equalization with improved switching median filter,” International Journal of Advanced Engineering Sciences and Technologies, Vol. 7, 2011, pp. 206211.
  • P. Kour, “Immage Processing using Discrete Wavelet Transformation”, International Journal of Electronics & Communication (IIJEC), Volume 3, Issue 1, January 2015, ISSN 2321-5984.
  • J. Turunen, “A wavelet-based method for estimating damping in power systems,”Ph.D. Thesis, Department of Electrical Engineering Power Transmission Systems, Aalto University, 2011.
  • J. Han, M. Kamber, J. Pei, “Data Mining: Concepts and Techniques”, Third Edition (The Morgan Kaufmann Series in Data Management), ISBN-13: 978-9380931913, 2011.
  • P. N. Tan, M. Steinbach, V. Kumar,” Introduction to Data Mining 1st Edition”, ISBN-13: 978-0321321367, Pearson Education, 2014.

ENHANCING BREAST CANCER DETECTION USING DATA MINING CLASSIFICATION TECHNIQUES

Yıl 2017, Cilt: 5 Sayı: 1, 310 - 316, 30.06.2017
https://doi.org/10.17261/Pressacademia.2017.605

Öz

Cancer is one of the crucial causes of death for both men and
women. All over the world, breast cancer is one of the leading cause of cancer
deaths in women. The most effective way to reduce cancer death is to detect it
earlier but the detection of cancer in early stages is not an easy process. As
result, many researches are focused on developing different systems for breast
cancer detection. In this paper we have discussed various data mining
approaches that have been utilized for breast cancer diagnosis and prognosis.
We have proposed a breast cancer prediction framework consisting of four main
modules: Data Collection, Data Preprocessing, Feature Selection, and
Classification. Evaluation results are provided as well. The goal is to find
the best combination for feature extraction algorithm and classification
algorithm, which will improve the accuracy of mammograms classification
process.



 

Kaynakça

  • V. Gaike, R. Mhaske, S. Sonawane, N. Akhter, P. D. Deshmukh, "Clustering of breast cancer tumor using third order GLCM feature", , vol. 00, no. , pp. 318-322, 2015
  • A. Sahar “Predicting the Serverity of Breast Masses with Data Mining Methods” International Journal of Computer Science Issues, Vol. 10, Issues 2, No 2, March 2013 ISSN (Print):1694-0814| ISSN (Online):1694-0784 www.IJCSI.org.
  • S. Sondele and I. Saini, "Classification of Mammograms Using Bidimensional Empirical Mode Decomposition Based Features and Artificial Neural Network", International Journal of Bio-Science and Bio-Technology, Vol.5, No.6 (2013), pp.171-180.
  • Z. K. Senturk and R. “Breast Cancer Diagnosis Via Data Mining: Performance Analysis of Seven Different Algorithms”, Computer Science & Engineering: An International Journal (CSEIJ), Vol. 4, No. 1, February 2014.
  • M. P. Sampat, M. K. Markey, and A. C. Bovik, "Computer-Aided Detection and Diagnosis in Mammography," in Handbook of Image and Video Processing, A. C. Bovik, Ed., 2nd ed, 2005.
  • Salama Gouda I., M. B. Abdelhalim, and Magdy Abd-elghany Zeid. "Experimental comparison of classifiers for breast cancer diagnosis." Computer Engineering & Systems (ICCES), 2012 Seventh International Conference on. IEEE, 2012.
  • Mittal Dishant, Dev Gaurav, and Sanjiban Sekhar Roy. "An effective hybridized classifier for breast cancer diagnosis." 2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM). IEEE, 2015.
  • M. Vasantha, S. Bharathi V and R. Dhamodharan, “Medical image feature, extraction, selection and classification”. Intl. J. Engg.Sci. & Technol. 2(6), 2071-2076, 2010.
  • S M Halawani “A study of digital mammograms by using clustering algorithms” Journal of Scientific & Industrial Research Vol. 71, September 2012, pp. 594-600.
  • B. Padmapriya and T. Velmurugan, "A survey on breast cancer analysis using data mining techniques," 2014 IEEE International Conference on Computational Intelligence and Computing Research, Coimbatore, 2014, pp. 1-4.
  • K. Kourou, T. P. Exarchos, K. P. Exarchos , M. V. Karamouzis, D. I. Fotiadis, “Machine learning applications in cancer prognosis and prediction”, Computational and Structural Biotechnology Journal 13 (2015) 8–17.
  • Cruz JA, Wishart DS. “Applications of Machine Learning in Cancer Prediction and Prognosis”. Cancer Informatics. 2006;2:59-77.
  • Mammographic Image Analysis, http://www.mammoimage.org/databases/. Last accessed: March, 2017.
  • R. Sharmila and R. Uma, “A new approach to image contrast enhancement using weighted threshold histogram equalization with improved switching median filter,” International Journal of Advanced Engineering Sciences and Technologies, Vol. 7, 2011, pp. 206211.
  • P. Kour, “Immage Processing using Discrete Wavelet Transformation”, International Journal of Electronics & Communication (IIJEC), Volume 3, Issue 1, January 2015, ISSN 2321-5984.
  • J. Turunen, “A wavelet-based method for estimating damping in power systems,”Ph.D. Thesis, Department of Electrical Engineering Power Transmission Systems, Aalto University, 2011.
  • J. Han, M. Kamber, J. Pei, “Data Mining: Concepts and Techniques”, Third Edition (The Morgan Kaufmann Series in Data Management), ISBN-13: 978-9380931913, 2011.
  • P. N. Tan, M. Steinbach, V. Kumar,” Introduction to Data Mining 1st Edition”, ISBN-13: 978-0321321367, Pearson Education, 2014.
Toplam 18 adet kaynakça vardır.

Ayrıntılar

Bölüm Makaleler
Yazarlar

Florije Ismaili Bu kişi benim

Luzana Shabani Bu kişi benim

Bujar Raufi Bu kişi benim

Jaumin Ajdari Bu kişi benim

Xhemal Zenuni Bu kişi benim

Yayımlanma Tarihi 30 Haziran 2017
Yayımlandığı Sayı Yıl 2017 Cilt: 5 Sayı: 1

Kaynak Göster

APA Ismaili, F., Shabani, L., Raufi, B., Ajdari, J., vd. (2017). ENHANCING BREAST CANCER DETECTION USING DATA MINING CLASSIFICATION TECHNIQUES. PressAcademia Procedia, 5(1), 310-316. https://doi.org/10.17261/Pressacademia.2017.605
AMA Ismaili F, Shabani L, Raufi B, Ajdari J, Zenuni X. ENHANCING BREAST CANCER DETECTION USING DATA MINING CLASSIFICATION TECHNIQUES. PAP. Haziran 2017;5(1):310-316. doi:10.17261/Pressacademia.2017.605
Chicago Ismaili, Florije, Luzana Shabani, Bujar Raufi, Jaumin Ajdari, ve Xhemal Zenuni. “ENHANCING BREAST CANCER DETECTION USING DATA MINING CLASSIFICATION TECHNIQUES”. PressAcademia Procedia 5, sy. 1 (Haziran 2017): 310-16. https://doi.org/10.17261/Pressacademia.2017.605.
EndNote Ismaili F, Shabani L, Raufi B, Ajdari J, Zenuni X (01 Haziran 2017) ENHANCING BREAST CANCER DETECTION USING DATA MINING CLASSIFICATION TECHNIQUES. PressAcademia Procedia 5 1 310–316.
IEEE F. Ismaili, L. Shabani, B. Raufi, J. Ajdari, ve X. Zenuni, “ENHANCING BREAST CANCER DETECTION USING DATA MINING CLASSIFICATION TECHNIQUES”, PAP, c. 5, sy. 1, ss. 310–316, 2017, doi: 10.17261/Pressacademia.2017.605.
ISNAD Ismaili, Florije vd. “ENHANCING BREAST CANCER DETECTION USING DATA MINING CLASSIFICATION TECHNIQUES”. PressAcademia Procedia 5/1 (Haziran 2017), 310-316. https://doi.org/10.17261/Pressacademia.2017.605.
JAMA Ismaili F, Shabani L, Raufi B, Ajdari J, Zenuni X. ENHANCING BREAST CANCER DETECTION USING DATA MINING CLASSIFICATION TECHNIQUES. PAP. 2017;5:310–316.
MLA Ismaili, Florije vd. “ENHANCING BREAST CANCER DETECTION USING DATA MINING CLASSIFICATION TECHNIQUES”. PressAcademia Procedia, c. 5, sy. 1, 2017, ss. 310-6, doi:10.17261/Pressacademia.2017.605.
Vancouver Ismaili F, Shabani L, Raufi B, Ajdari J, Zenuni X. ENHANCING BREAST CANCER DETECTION USING DATA MINING CLASSIFICATION TECHNIQUES. PAP. 2017;5(1):310-6.

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