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Evaluation of Learning Approaches Based on Convolutional Neural Networks for Mammogram Classification

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Smart Technologies, Systems and Applications (SmartTech-IC 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1154))

  • 436 Accesses

Abstract

Mammography is still considered the best screening method for detection, diagnosis and follow-up of breast cancer. A correct classification of mammographic findings demands a high expertise level of the clinician observer. For this, different Computer-aided Diagnosis systems have been developed to support the diagnosis tasks and reduce the inter or intra-observer variability caused by the complex visual information contained in mammograms. However, the classification of some findings (masses, calcifications) is still a difficult task. This work presents a methodological approach to evaluate the performance of the training process for different convolutional neural network configurations of the VGG16 Convolutional Neural Network architecture, designed to perform mammographic classification. For doing that, the impact of different learning strategies (focal loss, to deal with highly unbalance datasets, gradient clipping and learning transfer) is evaluated.

The proposed method was two-fold evaluated. First, the performance for classifying between normal and abnormal Regions of Interest (ROIs) extracted from the DDSM and CBIS-DDSM datasets was explored. After that, a multi-class problem was addressed, for which a set of 5-class was included according to well-known BI-RADS classification. The obtained results reported an average accuracy of 0.92 for the binary classification and a rate of accuracy of 0.85 for the 5-class classification (with 30 epochs), reducing the convergence time (23 and 30 epochs for both binary and multi-class classification tasks, respectively).

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Notes

  1. 1.

    One epoch is when an entire dataset is passed forward and backward through the neural network only once.

References

  1. Abdelhafiz, D., Yang, C., Ammar, R., Nabavi, S.: Deep convolutional neural networks for mammography: advances, challenges and applications. BMC Bioinformatics 20(11), 281 (2019)

    Article  Google Scholar 

  2. Bai, L., Zhao, Y., Huang, X.: A CNN accelerator on FPGA using depthwise separable convolution. IEEE Trans. Circuits Syst. II Express Briefs 65(10), 1415–1419 (2018)

    Article  Google Scholar 

  3. Basanth, M., Shettar, R.: Transfer learning on pre-trained deep convolutional neural network for classification of masses in mammograms. IOSR J. Comput. Eng. 19(50), e5 (2017)

    Google Scholar 

  4. Bendersky, E.: Depthwise separable convolutions for machine learning (2019). https://eli.thegreenplace.net/2018/depthwise-separable-convolutions-for-machine-learning/

  5. Bengio, Y.: Practical recommendations for gradient-based training of deep architectures. In: Montavon, G., Orr, G.B., Müller, K.-R. (eds.) Neural Networks: Tricks of the Trade. LNCS, vol. 7700, pp. 437–478. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35289-8_26

    Chapter  Google Scholar 

  6. Bocchi, L., Coppini, G., Nori, J., Valli, G.: Detection of single and clustered microcalcifications in mammograms using fractals models and neural networks. Med. Eng. Phys. 26(4), 303–312 (2004)

    Article  Google Scholar 

  7. Carneiro, G., Nascimento, J., Bradley, A.P.: Unregistered multiview mammogram analysis with pre-trained deep learning models. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 652–660. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_78

    Chapter  Google Scholar 

  8. Chawla, N.V.: Data mining for imbalanced datasets: an overview. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 875–886. Springer, Boston (2009). https://doi.org/10.1007/0-387-25465-X_40

    Chapter  Google Scholar 

  9. Dhungel, N., Carneiro, G., Bradley, A.P.: Automated mass detection in mammograms using cascaded deep learning and random forests. In: 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 1–8. IEEE (2015)

    Google Scholar 

  10. Dominguez, A.R., Nandi, A.K.: Detection of masses in mammograms via statistically based enhancement, multilevel-thresholding segmentation, and region selection. Comput. Med. Imaging Graph. 32(4), 304–315 (2008)

    Article  Google Scholar 

  11. Dumoulin, V., Visin, F.: A guide to convolution arithmetic for deep learning. arXiv preprint arXiv:1603.07285 (2016)

  12. Eltoukhy, M.M., Faye, I., Samir, B.B.: Breast cancer diagnosis in digital mammogram using multiscale curvelet transform. Comput. Med. Imaging Graph. 34(4), 269–276 (2010)

    Article  Google Scholar 

  13. Estabrooks, A., Jo, T., Japkowicz, N.: A multiple resampling method for learning from imbalanced data sets. Comput. Intell. 20(1), 18–36 (2004)

    Article  MathSciNet  Google Scholar 

  14. Gao, J., Jiang, Q., Zhou, B., Chen, D.: Convolutional neural networks for computer-aided detection or diagnosis in medical image analysis: an overview. Math. Biosci. Eng. 16(6), 6536–6561 (2019)

    Article  MathSciNet  Google Scholar 

  15. Gur, D., et al.: Computer-aided detection performance in mammographic examination of masses: assessment. Radiology 233(2), 418–423 (2004)

    Article  Google Scholar 

  16. Guyon, I.: A scaling law for the validation-set training-set size ratio, pp. 1–11. AT&T Bell Laboratories (1997)

    Google Scholar 

  17. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)

    Google Scholar 

  18. Hodges, J.: The significance probability of the smirnov two-sample test. Arkiv för Matematik 3(5), 469–486 (1958)

    Article  MathSciNet  MATH  Google Scholar 

  19. Hossin, M., Sulaiman, M.: A review on evaluation metrics for data classification evaluations. Int. J. Data Min. Knowl. Manage. Proc. 5(2), 1 (2015)

    Google Scholar 

  20. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)

  21. Iqbal, H.: PlotNeuralNet, December 2018. https://doi.org/10.5281/zenodo.2526396

  22. Jadoon, M.M., Zhang, Q., Haq, I.U., Butt, S., Jadoon, A.: Three-class mammogram classification based on descriptive CNN features. Biomed. Res. Int. 2017, 3640901 (2017)

    Article  Google Scholar 

  23. Kornblith, S., Shlens, J., Le, Q.V.: Do better ImageNet models transfer better? In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2661–2671 (2019)

    Google Scholar 

  24. Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)

    Google Scholar 

  25. Ma, N., Zhang, X., Zheng, H.T., Sun, J.: Shufflenet V2: practical guidelines for efficient CNN architecture design. In: The European Conference on Computer Vision (ECCV), September 2018

    Google Scholar 

  26. Matthews, B.W.: Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta (BBA) Protein Struct. 405(2), 442–451 (1975)

    Article  Google Scholar 

  27. Moayedi, F., Azimifar, Z., Boostani, R., Katebi, S.: Contourlet-based mammography mass classification using the SVM family. Comput. Biol. Med. 40(4), 373–383 (2010)

    Article  Google Scholar 

  28. Montavon, G., Orr, G.B., Müller, K.-R. (eds.): Neural Networks: Tricks of the Trade. LNCS, vol. 7700. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35289-8

    Book  Google Scholar 

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

    Article  Google Scholar 

  30. Narváez, F., Romero, E.: Breast mass classification using orthogonal moments. In: Maidment, A.D.A., Bakic, P.R., Gavenonis, S. (eds.) IWDM 2012. LNCS, vol. 7361, pp. 64–71. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31271-7_9

    Chapter  Google Scholar 

  31. Oliver, A., et al.: A review of automatic mass detection and segmentation in mammographic images. Med. Image Anal. 14(2), 87–110 (2010)

    Article  Google Scholar 

  32. Pascanu, R., Mikolov, T., Bengio, Y.: Understanding the exploding gradient problem. CoRR, abs/1211.5063 (2012)

    Google Scholar 

  33. Qian, W., Sun, W., Zheng, B.: Improving the efficacy of mammography screening: the potential and challenge of developing new computer-aided detection approaches. Expert Rev. Med. Devices 12(5), 497–499 (2015)

    Article  Google Scholar 

  34. Ramos-Pollán, R., et al.: Discovering mammography-based machine learning classifiers for breast cancer diagnosis. J. Med. Syst. 36(4), 2259–2269 (2012)

    Article  Google Scholar 

  35. Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211–252 (2015)

    Article  MathSciNet  Google Scholar 

  36. Sahiner, B., et al.: Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images. IEEE Trans. Med. Imaging 15(5), 598–610 (1996)

    Article  Google Scholar 

  37. Sarvazyan, A., Egorov, V., Son, J., Kaufman, C.: Article commentary: cost-effective screening for breast cancer worldwide: current state and future directions. Breast Cancer Basic Clin. Res. 1, BCBCR1–S774 (2008)

    Article  Google Scholar 

  38. Saslow, D., et al.: American cancer society guidelines for breast screening with MRI as an adjunct to mammography. CA Cancer J. Clin. 57(2), 75–89 (2007)

    Article  Google Scholar 

  39. Scuccimarra, E.A.: The Hypermedia Image Processing Reference (2018). https://www.kaggle.com/skooch/ddsm-mammography

  40. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  41. Singh, H.: Basics of Python and Scikit image. Practical Machine Learning and Image Processing, pp. 29–61. Apress, Berkeley (2019). https://doi.org/10.1007/978-1-4842-4149-3_3

    Chapter  Google Scholar 

  42. Smith, L.N.: Cyclical learning rates for training neural networks. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 464–472. IEEE (2017)

    Google Scholar 

  43. Solem, J.E.: Programming Computer Vision with Python: Tools and Algorithms for Analyzing Images. O’Reilly Media, Inc., Sebastopol (2012)

    Google Scholar 

  44. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

  45. Tabar, L., Yen, M.F., Vitak, B., Chen, H.H.T., Smith, R.A., Duffy, S.W.: Mammography service screening and mortality in breast cancer patients: 20-year follow-up before and after introduction of screening. The Lancet 361(9367), 1405–1410 (2003)

    Article  Google Scholar 

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Authors and Affiliations

  1. Universidad Central, Bogotá, 110321, Colombia

    Roberto Arias & Hugo Franco

  2. Universidad Politécnica Salesiana, Quito, 111221, Ecuador

    Fabián Narváez

Authors
  1. Roberto Arias
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  2. Fabián Narváez
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  3. Hugo Franco
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Corresponding author

Correspondence to Hugo Franco .

Editor information

Editors and Affiliations

  1. Universidad Politécnica Salesiana, Quito, Ecuador

    Fabián R. Narváez

  2. Universidad Politécnica Salesiana, Quito, Ecuador

    Diego F. Vallejo

  3. Universidad Politécnica Salesiana, Quito, Ecuador

    Paulina A. Morillo

  4. Universidad Politécnica Salesiana, Quito, Ecuador

    Julio R. Proaño

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Arias, R., Narváez, F., Franco, H. (2020). Evaluation of Learning Approaches Based on Convolutional Neural Networks for Mammogram Classification. In: Narváez, F., Vallejo, D., Morillo, P., Proaño, J. (eds) Smart Technologies, Systems and Applications. SmartTech-IC 2019. Communications in Computer and Information Science, vol 1154. Springer, Cham. https://doi.org/10.1007/978-3-030-46785-2_22

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  • DOI: https://doi.org/10.1007/978-3-030-46785-2_22

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  • Print ISBN: 978-3-030-46784-5

  • Online ISBN: 978-3-030-46785-2

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