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Comparison of Thermography and 3D Mammography Screening and Classification Techniques for Breast Cancer

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Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB) (ISMAC 2018)

Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 30))

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Abstract

Breast cancer, without doubt is one of the leading reasons for fatality among women in the world after lung cancer. Awareness and accessibility to better screening and treatment protocols will have a major impact in improving the survival rates. Moving away from the traditional methods of mammography and biopsy methods, newer techniques provide faster and efficient results to ensure early start of treatment. Therefore, a comparison study has been performed to weigh the pros and cons of thermography and 3D mammography as screening methods, followed by their respective processing and classification procedures. The ease of screening, extent of radiation, percentage of false positives, efficient segmentation, clustering, and novel classification are all considered and a conclusive result is obtained determining the better of the two processes. This could potentially revolutionize the way breast cancer is diagnosed and treated for women of all ages and walks of life.

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References

  1. Gonzalez RC, Woods RE. Digital image processing, 3rd edn. Pearson Education

    Google Scholar 

  2. Akinyemiju TF (2013) Risk of asynchronous contralateral breast cancer: multiple approaches for a complex issue. Gland Surg 2(2):110–113

    Google Scholar 

  3. Cowley G (2017) Mammography vs. thermography: comparing the benefits. Medical News Today

    Google Scholar 

  4. Geoge KMJ, Dhas DAS (2017) Preprocessing filters for mammogram images: a review. In: Emerging devices and smart systems (ICEDSS). ISBN: 978-1-5090-5555-5. IEEE Xplore 19 Oct 2017

    Google Scholar 

  5. Ganesan K, Acharya UR, Chua CK, Min LC, Abraham TK (2014) Automated Diagnosis of Mammogram images of breast cancer using discrete wavelet transform and spherical wavelet transform features. Technol Cancer Res Treat 13(6):605–615

    Google Scholar 

  6. Mahmoudzadeh E, Zekri M, Montazeri MA, Sadri S, Dabbaggh ST (2016) Directional SUSAN image boundary detection of breast thermogram. IET Image Process 552–560

    Google Scholar 

  7. Shmmala FA, Ashour W (2013) Color based image segmentation using different versions of K-means in two spaces. Global Adv Res J Eng Technol Innov 1(9):030–041. (ISSN: 2315-5124)

    Google Scholar 

  8. Shukla M, Changlani S, A Comparative study of wavelet and curvelet transform for image

    Google Scholar 

  9. Catanzaro BC, Sundaram N, Keutzer K (2008) Fast support vector machine training and classification on graphic processors. In: UCB/EECS

    Google Scholar 

  10. Phan J, Moffitt R, Dale J, Petros J, Young A, Wang M (2005) Improvement of SVM algorithm for microarray analysis using intelligent parameter selection. In: 2005 IEEE-EMBS 2005 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4838–4841

    Google Scholar 

  11. Shrivastava P, Singh P, Shrivastava G (2014) Image classification using SOM and SVM feature extraction, (IJCSIT) 5(1):264–271

    Google Scholar 

  12. Cervantes J, Li X, Yu W, Li K (2008) Support vector machine classification of large data sets via minimum enclosing ball clustering. Neurocomputing 71(4–6):611–619

    Google Scholar 

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Correspondence to Sureshkumar Krithika .

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Krithika, S., Suriya, K., Karthika, R., Priyadharshini, S. (2019). Comparison of Thermography and 3D Mammography Screening and Classification Techniques for Breast Cancer. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds) Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-00665-5_121

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  • DOI: https://doi.org/10.1007/978-3-030-00665-5_121

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00664-8

  • Online ISBN: 978-3-030-00665-5

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