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An Interpretable SVM Based Model for Cancer Prediction in Mammograms

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Communication, Networks and Computing (CNC 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 839))

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Abstract

Machine learning algorithms are inherently not interpretable, and this poses a problem in risk-averse applications of machine learning. Mammographic images are widely used tool to predict breast cancer. Various machine learning algorithms like SVM, RBFNN are used to detect the mass in the mammographic images and classify for cancer, but the classification by SVMs are not intuitive. Our aim is to counter this problem by employing a novel method of using multiple SVMs to elucidate the area affected by cancer. We also color-code the patches for further clarification.

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Notes

  1. 1.

    n depends on appropriate number of patches in which a mammogram can be divided.

References

  1. Ribeiro, M.T., Singh, S., Guestrin, C.: Why should i trust you?: Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144. ACM (2016)

    Google Scholar 

  2. Nguyen, D.H., Le, M.T.: Improving the interpretability of support vector machines based fuzzy rules. arXiv preprint arXiv:1408.5246 (2014)

  3. Barakat, N.H., Bradley, A.P.: Rule extraction from support vector machines: a sequential covering approach. IEEE Trans. Knowl. Data Eng. 19(6), 729–741 (2007)

    Article  Google Scholar 

  4. He, J., Hu, H.J., Harrison, R., Tai, P.C., Pan, Y.: Rule generation for protein secondary structure prediction with support vector machines and decision tree. IEEE Trans. Nanobioscience 5(1), 46–53 (2006)

    Article  Google Scholar 

  5. Fu, S., ShengYang, G., Hou, Z., Liang, Z., Tan, M.: Multiple kernel learning from sets of partially matching image features. In: 19th International Conference on Pattern Recognition, 2008, ICPR 2008, pp. 1–4. IEEE (2008)

    Google Scholar 

  6. Jakulin, A., Možina, M., Demšar, J., Bratko, I., Zupan, B.: Nomograms for visualizing support vector machines. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 108–117. ACM (2005)

    Google Scholar 

  7. Pereira, C., Dourado, A.: On the complexity and interpretability of support vector machines for process modeling. In: Proceedings of the 2002 International Joint Conference on Neural Networks, IJCNN 2002. vol. 3, pp. 2204–2209. IEEE (2002)

    Google Scholar 

  8. Oliver, A., Lladó, X., Freixenet, J., Martí, J.: False positive reduction in mammographic mass detection using local binary patterns. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007. LNCS, vol. 4791, pp. 286–293. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-75757-3_35

    Chapter  Google Scholar 

  9. Lehman, C.D., Wellman, R.D., Buist, D.S., Kerlikowske, K., Tosteson, A.N., Miglioretti, D.L.: Diagnostic accuracy of digital screening mammography with and without computer-aided detection. JAMA Intern. Med. 175(11), 1828–1837 (2015)

    Article  Google Scholar 

  10. Pratiwi, M., Harefa, J., Nanda, S., et al.: Mammograms classification using graylevel co-occurrence matrix and radial basis function neural network. Procedia Comput. Sci. 59, 83–91 (2015)

    Article  Google Scholar 

  11. Gardezi, S.J.S., Faye, I., Bornot, J.M.S., Kamel, N., Hussain, M.: Mammogram classification using dynamic time warping. Multimed. Tools Appl. 77, 1–22 (2017)

    Google Scholar 

  12. Ibrahim, A.M., Baharudin, B.: Classification of mammogram images using shearlet transform and kernel principal component analysis. In: 2016 3rd International Conference on Computer and Information Sciences (ICCOINS), pp. 340–344. IEEE (2016)

    Google Scholar 

  13. Azar, A.T., El-Said, S.A.: Performance analysis of support vector machines classifiers in breast cancer mammography recognition. Neural Comput. Appl. 24(5), 1163–1177 (2014)

    Article  Google Scholar 

  14. Ferreira, P., Dutra, I., Salvini, R., Burnside, E.: Interpretable models to predict breast cancer. In: 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1507–1511. IEEE (2016)

    Google Scholar 

  15. Christoyianni, I., Koutras, A., Dermatas, E., Kokkinakis, G.: Computer aided diagnosis of breast cancer in digitized mammograms. Comput. Med. Imaging Graph. 26(5), 309–319 (2002)

    Article  Google Scholar 

  16. Suckling, J., et al.: The mammographic image analysis society digital mammogram database. In: Exerpta Medica. International Congress Series, vol. 1069, pp. 375–378 (1994)

    Google Scholar 

  17. Suckling, J., et al.: Mammographic image analysis society (mias) database v1. 21 (2015)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Information Technology, Indian Institute of Information Technology, Allahabad, Allahabad, 211012, Uttar Pradesh, India

    Abhishek Verma, Prashant Shukla,  Abhishek & Shekhar Verma

Authors
  1. Abhishek Verma
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  2. Prashant Shukla
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  3. Abhishek
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  4. Shekhar Verma
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Corresponding author

Correspondence to Prashant Shukla .

Editor information

Editors and Affiliations

  1. Indian Institute of Information Technology, Allahabad, India

    Shekhar Verma

  2. ITM University, Gwalior, India

    Ranjeet Singh Tomar

  3. ITM University, Gwalior, India

    Brijesh Kumar Chaurasia

  4. Indian Institute of Information Technology, Allahabad, India

    Vrijendra Singh

  5. School of Information Technology, Deakin University, Burwood, VIC, Australia

    Jemal Abawajy

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Verma, A., Shukla, P., Abhishek, Verma, S. (2019). An Interpretable SVM Based Model for Cancer Prediction in Mammograms. In: Verma, S., Tomar, R., Chaurasia, B., Singh, V., Abawajy, J. (eds) Communication, Networks and Computing. CNC 2018. Communications in Computer and Information Science, vol 839. Springer, Singapore. https://doi.org/10.1007/978-981-13-2372-0_39

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  • DOI: https://doi.org/10.1007/978-981-13-2372-0_39

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  • Print ISBN: 978-981-13-2371-3

  • Online ISBN: 978-981-13-2372-0

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