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A Classifier Ensemble Method for Breast Tumor Classification Based on the BI-RADS Lexicon for Masses in Mammography

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XXVII Brazilian Congress on Biomedical Engineering (CBEB 2020)

Part of the book series: IFMBE Proceedings ((IFMBE,volume 83))

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Abstract

A computer-aided diagnosis (CAD) system is a tool to assist clinicians in interpreting medical images. In mammography, CADs provide a classification of tumors to distinguish between benign and malignant cases, aiming to support the clinical conduct. Nevertheless, CADs disregard informing about the internal criteria utilized to classify breast tumors, particularly, compatible with the Breast Imaging-Reporting and Data System (BI-RADS). In this context, we propose a new scheme of tumor classification based on the BI-RADS lexicon for masses. The terms of shape, margin, and density are modeled using specific feature sets to provide different perspectives of the tumor in terms of benign and malignant findings. The outcomes of the three models are further used for the final histopathological classification of the tumor. The proposed method is compared with two conventional CAD systems that classify tumors using a single feature set. The results show that the proposed method obtains 90% accuracy, whereas the two conventional CADs reach an accuracy of 89% and 76%. Therefore, the proposed method is suitable for the histopathological classification of tumors by using the information provided by the three models of the BI-RADS lexicon for masses.

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Notes

  1. 1.

    http://marathon.csee.usf.edu/Mammography/Database.html.

  2. 2.

    https://www.csie.ntu.edu.tw/~cjlin/libsvm/.

  3. 3.

    http://www.robots.ox.ac.uk/~vgg/research/texclass/filters.html.

References

  1. Rodriguez-Rojas J, Garza-Montemayor M, Trevino-Alvarado V, Tamez-Pena JG (2013) Predictive features of breast cancer on Mexican screening mammography patients. In: SPIE medical imaging: computer-aided diagnosis, vol 8670, pp 534–542

    Google Scholar 

  2. Chávarri-Guerra Y, Villarreal-Garza C, Liedke PER et al (2012) Breast cancer in Mexico: a growing challenge to health and the health system. Lancet Oncol 13:e335–e343

    Article  Google Scholar 

  3. Laroussi MG, Ayed NGB, Masmoudi AD, Masmoudi DS (2013) Diagnosis of masses in mammographic images based on Zernike moments and local binary attributes. In: 2013 world congress on computer and information technology (WCCIT), pp 1–6

    Google Scholar 

  4. Redondo A, Maciá F, Ferrer F et al (2012) Inter- and intraradiologist variability in the BI-RADS assessment and breast density categories for screening mammograms. Br J Radiol 85:1465–1470

    Article  Google Scholar 

  5. Gaona E, Arenas V, Bernal MS, Tzitzitlini L, Molina-Frechero N, Franco JG (2017) Efficiency indicators of mammography in the detection of breast cancer early stages: exploratory study in Mexico. Int J Appl Sci Technol 7:32–36

    Google Scholar 

  6. Ayed NGB, Masmoudi AD, Sellami D, Abid R (2015) New developments in the diagnostic procedures to reduce prospective biopsies breast. In: 2015 international conference on advances in biomedical engineering (ICABME), pp 205–208

    Google Scholar 

  7. Molnar C (2019) Interpretable machine learning. https://christophm.github.io/interpretable-ml-book/

  8. Gómez-Flores W, Hernández-López J (2020) Assessment of the invariance and discriminant power of morphological features under geometric transformations for breast tumor classification. Comput Methods Programs Biomed 185:105173

    Article  Google Scholar 

  9. Smeraldi F (2002) Ranklets: orientation selective non-parametric features applied to face detection. In: Object recognition supported by user interaction for service robots, vol 3, pp 379–382

    Google Scholar 

  10. Haralick RM (1976) Texture features for image classification. IEEE Trans Syst Man Cybern 6:269–285

    Google Scholar 

  11. Flores WG, de Albuquerque Pereira WC, Infantosi AFC (2015) Improving classification performance of breast lesions on ultrasonography. Pattern Recogn 48:1125–1136

    Article  Google Scholar 

  12. Chang C-C, Lin C-J (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2:27:1–27:27. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm

  13. Zhou Z-H (2012) Ensemble methods: foundations and algorithms, 1st edn. Chapman and Hall/CRC

    Google Scholar 

  14. Suhail Z, Hamidinekoo A, Denton ER, Zwiggelaar R (2017) A Texton-based approach for the classification of benign and malignant masses in mammograms. In: Medical image understanding and analysis. Springer International Publishing, pp 355–364

    Google Scholar 

  15. Sokolova M, Lapalme G (2009) A systematic analysis of performance measures for classification tasks. Inf Process Manag 45:427–437

    Article  Google Scholar 

  16. Stavros AT (2004) Ultrasound of solid breast nodules: distinguihing benign from malignant, vol 447. Lippincott Williams & Wilkins, PA

    Google Scholar 

  17. Narváez F, Díaz G, Poveda C, Romero E (2017) An automatic BI-RADS description of mammographic masses by fusing multiresolution features. Exp Syst Appl 74:82–95

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thanks to the National Council of Science and Technology (CONACyT, Mexico) for the research scholar grant (No. 463795) and also to the Fondo SEP-Cinvestav 2018 (No. FidSC2018/145).

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Authors and Affiliations

  1. Unidad Tamaulipas, Centro de Investigación y de Estudios Avanzados del IPN, Parque TECNOTAM, km. 5.5 Cd. Victoria-Soto La Marina, Ciudad Victoria, Tamaulipas, Mexico

    Juanita Hernández-López & Wilfrido Gómez-Flores

Authors
  1. Juanita Hernández-López
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  2. Wilfrido Gómez-Flores
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Corresponding author

Correspondence to Juanita Hernández-López .

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Editors and Affiliations

  1. Department of Electrical Engineering, Universidade Federal do Espírito Santo, Vitória, Brazil

    Teodiano Freire Bastos-Filho

  2. Department of Electrical Engineering, Universidade Federal do Espírito Santo, Vitória, Brazil

    Eliete Maria de Oliveira Caldeira

  3. Department of Electrical Engineering, Universidade Federal do Espírito Santo, Vitória, Brazil

    Anselmo Frizera-Neto

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Hernández-López, J., Gómez-Flores, W. (2022). A Classifier Ensemble Method for Breast Tumor Classification Based on the BI-RADS Lexicon for Masses in Mammography. In: Bastos-Filho, T.F., de Oliveira Caldeira, E.M., Frizera-Neto, A. (eds) XXVII Brazilian Congress on Biomedical Engineering. CBEB 2020. IFMBE Proceedings, vol 83. Springer, Cham. https://doi.org/10.1007/978-3-030-70601-2_240

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  • DOI: https://doi.org/10.1007/978-3-030-70601-2_240

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-70600-5

  • Online ISBN: 978-3-030-70601-2

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