Advertisement

Breast Cancer Multi-classification through Deep Neural Network and Hierarchical Classification Approach

  • Ghulam MurtazaEmail author
  • Liyana ShuibEmail author
  • Ghulam Mujtaba
  • Ghulam Raza
Article
  • 30 Downloads

Abstract

Breast cancer (BC) is the third leading cause of deaths in women globally. In general, histopathology images are recommended for early diagnosis and detailed analysis for BC. Thus, state-of-the-art classification models are required for the early prediction of BC using histopathology images. This study aims to develop an accurate and computationally feasible classification model named Biopsy Microscopic Image Cancer Network (BMIC_Net) to classify BC into eight distinct subtypes through deep learning (DL) and hierarchical classification approach. For experiments, the publicly available dataset BreakHis is used and splitted into training and testing set. Furthermore, data augmentation was performed on training set only and 4096 result-oriented features were extracted through DL. In order to improve the classification performance, feature reduction schemes were experimented to elicit the most discriminative feature subset. Finally, six machine-learning algorithms were analyzed to acquire the best results. The experimental results revealed that BMIC_Net outperformed existing baseline models by obtaining the highest accuracy of 95.48% for first-level classifier and 94.62% and 92.45% for second-level classifiers. Thus, this model can be deployed on a normal desktop machine in any healthcare center of less privileged areas in under-developing countries to serve as second opinion for breast cancer classification.

Keywords

Breast Cancer Deep Learning and Transfer Learning Hierarchical Classification Histopathology Images Image Classification Convolutional Neural Network 

Notes

Acknowledgements

This research was fully funded by the University Malaya Research Grant – Frontier Science (Grant No: RG380-17AFR).

References

  1. 1.
    Abdi H, Williams LJ (2010) Principal component analysis. Wiley interdiscip rev: comput stat 2(4):433–459CrossRefGoogle Scholar
  2. 2.
    Aggarwal CC, Zhai C (2012) Mining text data: Springer Science & Business MediaGoogle Scholar
  3. 3.
    Al-masni MA, Al-antari MA, Park JM, Gi G, Kim TY, Rivera P, … Kim TS (2018) Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system. Comput Methods Prog Biomed 157:85–94.  https://doi.org/10.1016/j.cmpb.2018.01.017 CrossRefGoogle Scholar
  4. 4.
    Antropova N, Huynh BQ, Giger ML (2017) A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets. Med Phys 44(10):5162–5171.  https://doi.org/10.1002/mp.12453 CrossRefGoogle Scholar
  5. 5.
    Babu JS, Sukumar LB, Anandan K (2013) Quantitative Analysis of Digitized Mammograms Using Nonsubsampled Contourlets and Evolutionary Extreme Learning Machine. J Med Imaging and Health Inf 3(2):206–213CrossRefGoogle Scholar
  6. 6.
    Bayramoglu N, Kannala J, Heikkilä J (2016, 4–8 Dec. 2016) Deep learning for magnification independent breast cancer histopathology image classification. Paper presented at the 2016 23rd International Conference on Pattern Recognition (ICPR)Google Scholar
  7. 7.
    Bovis K, Singh S, Fieldsend J, Pinder C (2000) Identification of masses in digital mammograms with MLP and RBF nets. Paper presented at the Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference onGoogle Scholar
  8. 8.
    Buciu I, Gacsadi A (2009) Gabor wavelet based features for medical image analysis and classification. Paper presented at the Applied Sciences in Biomedical and Communication Technologies, 2009. ISABEL 2009. 2nd International Symposium onGoogle Scholar
  9. 9.
    Buciu I, Gacsadi A (2011) Directional features for automatic tumor classification of mammogram images. Biomed Signal Process Control 6(4):370–378CrossRefGoogle Scholar
  10. 10.
    Chougrad H, Zouaki H, Alheyane O (2018) Deep Convolutional Neural Networks for breast cancer screening. Comput Methods Prog Biomed 157:19–30.  https://doi.org/10.1016/j.cmpb.2018.01.011 CrossRefGoogle Scholar
  11. 11.
    Cruz-Roa A, Gilmore H, Basavanhally A, Feldman M, Ganesan S, Shih NNC, … Madabhushi A (2017) Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent. Sci Rep 7.  https://doi.org/10.1038/srep46450
  12. 12.
    Dimitropoulos K, Barmpoutis P, Zioga C, Kamas A, Patsiaoura K, Grammalidis N (2017) Grading of invasive breast carcinoma through Grassmannian VLAD encoding. PLoS One 12(9):e0185110.  https://doi.org/10.1371/journal.pone.0185110 CrossRefGoogle Scholar
  13. 13.
    Domingos P (2012) A few useful things to know about machine learning. Commun ACM 55(10):78–87CrossRefGoogle Scholar
  14. 14.
    Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874MathSciNetCrossRefGoogle Scholar
  15. 15.
    Fukunaga K (2013) Introduction to statistical pattern recognition: ElsevierGoogle Scholar
  16. 16.
    Gurcan MN, Boucheron LE, Can A, Madabhushi A, Rajpoot NM, Yener B (2009) Histopathological image analysis: A review. IEEE Rev Biomed Eng 2:147–171CrossRefGoogle Scholar
  17. 17.
    Han Z, Wei B, Zheng Y, Yin Y, Li K, Li S (2017) Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model. Sci Rep 7:4172.  https://doi.org/10.1038/s41598-017-04075-z CrossRefGoogle Scholar
  18. 18.
    Hand DJ, Till RJ (2001) A simple generalisation of the area under the ROC curve for multiple class classification problems. Mach Learn 45(2):171–186CrossRefGoogle Scholar
  19. 19.
    Jiang J, Ma J, Wang Z, Chen C, Liu X (2018) Hyperspectral Image Classification in the Presence of Noisy Labels (Vol. PP)Google Scholar
  20. 20.
    Kasban H, El-Bendary M, Salama D (2015) A comparative study of medical imaging techniques. Int J Inf Sci Intell Syst 4:37–58Google Scholar
  21. 21.
    Kent JT (1983) Information gain and a general measure of correlation. Biometrika 70(1):163–173.  https://doi.org/10.1093/biomet/70.1.163 MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Khosravi P, Kazemi E, Imielinski M, Elemento O, Hajirasouliha I (2018) Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images. Ebiomedicine 27:317–328.  https://doi.org/10.1016/j.ebiom.2017.12.026 CrossRefGoogle Scholar
  23. 23.
    Kotsiantis SB, Zaharakis I, Pintelas P (2007) Supervised machine learning: A review of classification techniques. Emerg artif intell appl comput eng 160:3–24Google Scholar
  24. 24.
    Kowal M, Filipczuk P, Obuchowicz A, Korbicz J, Monczak R (2013) Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images. Comput Biol Med 43(10):1563–1572.  https://doi.org/10.1016/j.compbiomed.2013.08.003 CrossRefGoogle Scholar
  25. 25.
    Kozegar E, Soryani M, Minaei B, Domingues I (2013) Assessment of a novel mass detection algorithm in mammograms. J Cancer Res Ther 9(4):592CrossRefGoogle Scholar
  26. 26.
    Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Paper presented at the Advances in neural information processing systemsGoogle Scholar
  27. 27.
    Kuramochi M, Karypis G (2005) Gene classification using expression profiles: A feasibility study. Int J Artif Intell Tools 14(04):641–660CrossRefGoogle Scholar
  28. 28.
    Litjens G, Sanchez CI, Timofeeva N, Hermsen M, Nagtegaal I, Kovacs I, … van der Laak J (2016) Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci Rep 6.  https://doi.org/10.1038/srep26286
  29. 29.
    Loukas C, Kostopoulos S, Tanoglidi A, Glotsos D, Sfikas C, Cavouras D (2013, 829461) Breast Cancer Characterization Based on Image Classification of Tissue Sections Visualized under Low Magnification. Comput Math Methods Med 2013.  https://doi.org/10.1155/2013/829461
  30. 30.
    Lu T, Chen X, Zhang Y, Chen C, Xiong Z (2018) SLR: Semi-coupled locality constrained representation for very low resolution face recognition and super resolution (Vol. PP)Google Scholar
  31. 31.
    Ma Y, Li C, Li H, Mei X, Ma J (2018) Hyperspectral Image Classification With Discriminative Kernel Collaborative Representation and Tikhonov Regularization. IEEE Geosci Remote Sens Lett 15(4):587–591.  https://doi.org/10.1109/LGRS.2018.2800080 CrossRefGoogle Scholar
  32. 32.
    Nahid A-A, Mehrabi MA, Kong Y (2018) Histopathological Breast Cancer Image Classification by Deep Neural Network Techniques Guided by Local Clustering. BioMed Research International, 2018 Google Scholar
  33. 33.
    Naik S, Doyle S, Agner S, Madabhushi A, Feldman M, Tomaszewski J (2008) Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology. Paper presented at the Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium onGoogle Scholar
  34. 34.
    Provost FJ, Fawcett T (1997) Analysis and visualization of classifier performance: comparison under imprecise class and cost distributions. Paper presented at the KDD.Google Scholar
  35. 35.
    Provost FJ, Fawcett T, Kohavi R (1998) The case against accuracy estimation for comparing induction algorithms. Paper presented at the ICMLGoogle Scholar
  36. 36.
    Rabidas R, Midya A, Chakraborty J (2018) Neighborhood Structural Similarity Mapping for the Classification of Masses in Mammograms. IEEE J Biomed Health Informa:1–1.  https://doi.org/10.1109/JBHI.2017.2715021
  37. 37.
    Rennie JD, Shih L, Teevan J, Karger DR (2003) Tackling the poor assumptions of naive bayes text classifiers. Paper presented at the Proceedings of the 20th international conference on machine learning (ICML-03)Google Scholar
  38. 38.
    Ribli D, Horvath A, Unger Z, Pollner P, Csabai I (2018) Detecting and classifying lesions in mammograms with Deep Learning. Sci Rep 8.  https://doi.org/10.1038/s41598-018-22437-z
  39. 39.
    Samah AA, Fauzi MFA, Mansor S (2017, 12–14 Sept. 2017) Classification of benign and malignant tumors in histopathology images. Paper presented at the 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)Google Scholar
  40. 40.
    Shen D, Wu G, Suk H-I (2017) Deep Learning in Medical Image Analysis. Annu Rev Biomed Eng 19:221–248.  https://doi.org/10.1146/annurev-bioeng-071516-044442 CrossRefGoogle Scholar
  41. 41.
    Spanhol FA, Cavalin PR, Oliveira LS, Petitjean C, Heutte L (2017) Deep Features for Breast Cancer Histopathological Image Classification. Paper presented at the Systems, Man, and Cybernetics (SMC), 2017 IEEE International Conference onGoogle Scholar
  42. 42.
    Spanhol FA, Oliveira LS, Petitjean C, Heutte L (2016) A dataset for breast cancer histopathological image classification. IEEE Trans Biomed Eng 63(7):1455–1462CrossRefGoogle Scholar
  43. 43.
    Surendiran B, Vadivel A (2010) Feature selection using stepwise ANOVA discriminant analysis for mammogram mass classification. Int J Recent Trends Eng Technol 3(2):55–57Google Scholar
  44. 44.
    Swiniarski RW, Lim HK, Shin JH, Skowron A (2006) Independent Component Analysis, Princpal Component Analysis and Rough Sets in Hybrid Mammogram Classification. Paper presented at the IPCVGoogle Scholar
  45. 45.
    Wan T, Cao J, Chen J, Qin Z (2017) Automated grading of breast cancer histopathology using cascaded ensemble with combination of multi-level image features. Neurocomputing 229:34–44.  https://doi.org/10.1016/j.neucom.2016.05.084 CrossRefGoogle Scholar
  46. 46.
    Wang P, Hu X, Li Y, Liu Q, Zhu X (2016) Automatic cell nuclei segmentation and classification of breast cancer histopathology images. Signal Process 122:1–13.  https://doi.org/10.1016/j.sigpro.2015.11.011 CrossRefGoogle Scholar
  47. 47.
    WHO, W. H. O. (2018) World Cancer ReportGoogle Scholar
  48. 48.
    Witten IH, Frank E, Hall MA, Pal CJ (2016) Data Mining: Practical machine learning tools and techniques: Morgan KaufmannGoogle Scholar
  49. 49.
    Wolpert DH, Macready WG (1995) No free lunch theorems for search. Retrieved fromGoogle Scholar
  50. 50.
    Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82.  https://doi.org/10.1109/4235.585893 CrossRefGoogle Scholar
  51. 51.
    Zhang Y-D, Pan C, Chen X, Wang F (2018) Abnormal breast identification by nine-layer convolutional neural network with parametric rectified linear unit and rank-based stochastic pooling. J Comput Sc 27:57–68.  https://doi.org/10.1016/j.jocs.2018.05.005 CrossRefGoogle Scholar
  52. 52.
    Zhang Y, Tomuro N, Furst J, Raicu DS (2012) Building an ensemble system for diagnosing masses in mammograms. Int J Comput Assist Radiol Surg 7(2):323–329CrossRefGoogle Scholar
  53. 53.
    Zhang Y, Wu X, Lu S, Wang H, Phillips P, Wang S (2016) Smart detection on abnormal breasts in digital mammography based on contrast-limited adaptive histogram equalization and chaotic adaptive real-coded biogeography-based optimization. SIMULATION 92(9):873–885.  https://doi.org/10.1177/0037549716667834 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Information Systems, Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala LumpurMalaysia
  2. 2.Department of Computer ScienceSukkur IBA UniversitySukkurPakistan
  3. 3.St. James’s HospitalDublinIreland

Personalised recommendations