Skip to main content

Advertisement

SpringerLink
Log in

A comparative study of breast cancer tumor classification by classical machine learning methods and deep learning method

  • Special Issue Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

In contemporary times, machine learning is being used in almost every field due to its better performance. Here, we consider different machine learning methods such as logistic regression, random forest, support vector classifier (SVC), AdaBoost classifier, bagging classifier, voting classifier, and Xception model to classify the breast cancer tumor and evaluate their performances. We used a standard dataset, i.e., breast Histopathology images, that has more than two lakhs color patches, each patch of size \(50\times 50\) scanned at the resolution of 40\(\times \). We use 60% of the above-mentioned dataset for training, 20% for validation, and 20% testing to all above-mentioned classifiers. The logistic regression classifier provides the scores of each precision, recall, and F1 measure as 0.72. The random forest method provides the score of each precision, recall, and F1 score as 0.80. The bagging and voting classifiers both have the values of each precision, recalls, and F1 scores as 0.81. In this case, both SVC and AdaBoost classifiers have the score of each precision, recall, and F1 score as 0.82, whereas in the case of the deep learning method, Xception model is used to have the score of each precision, recall, and F1 measure as 0.90 in the same condition. Thus, the Xception method performs the best among all mentioned methods in terms of each of the performance measures, i.e., precision, recall, and F1 score for the classification of breast cancer tumors. Thus, the importance of this research work is that we can classify tumors more accurately in less time. It may increase awareness of people toward breast cancer and decrease fears of tumors.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Notes

  1. https://www.who.int/cancer/prevention/diagnosis-screening/breast-cancer/en/.

References

  1. Saki, F., Tahmasbi, A., Soltanian-Zadeh, H., Shokouhi, S.B.: Fast opposite weight learning rules with application in breast cancer diagnosis. Comput. Biol. Med. 43(1), 32–41 (2013)

    Google Scholar 

  2. Mousa, D.S.A., Mello-Thoms, C., Ryan, E.A., Lee, W.B., Pietrzyk, M.W., Reed, W.M., Heard, R., Poulos, A., Tan, J., Li, Y., et al.: Mammographic density and cancer detection: does digital imaging challenge our current understanding? Acad. Radiol. 21(11), 1377–1385 (2014)

    Google Scholar 

  3. Anitha, J., Peter, J.D.: Mammogram segmentation using maximal cell strength updation in cellular automata. Med. Biol. Eng. Comput. 53(8), 737–749 (2015)

    Google Scholar 

  4. Sirinukunwattana, K., Snead, D.R., Rajpoot, N.M.: A stochastic polygons model for glandular structures in colon histology images. IEEE Trans. Med. Imaging 34(11), 2366–2378 (2015)

    Google Scholar 

  5. Farjam, R., Soltanian-Zadeh, H., Jafari-Khouzani, K., Zoroofi, R.A.: An image analysis approach for automatic malignancy determination of prostate pathological images. Cytom. Part B Clin. Cytom. J. Int. Soc. Anal. Cytol. 72(4), 227–240 (2007)

    Google Scholar 

  6. Naik, S., Doyle, S., Agner, S., Madabhushi, A., Feldman, M., Tomaszewski, J.: Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology. In: 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE, pp. 284–287 (2008)

  7. Monaco, J.P., Tomaszewski, J.E., Feldman, M.D., Hagemann, I., Moradi, M., Mousavi, P., Boag, A., Davidson, C., Abolmaesumi, P., Madabhushi, A.: High-throughput detection of prostate cancer in histological sections using probabilistic pairwise markov models. Med. Image Anal. 14(4), 617–629 (2010)

    Google Scholar 

  8. Peng, Y., Jiang, Y., Eisengart, L., Healy, M.A., Straus, F.H., Yang, X.J.: Computer-aided identification of prostatic adenocarcinoma: segmentation of glandular structures. J. Pathol. Inf. 2, 33 (2011)

    Google Scholar 

  9. Rashid, S., Fazli, L. Boag, A., Siemens, R., Abolmaesumi, P., Salcudean, S.E.: Separation of benign and malignant glands in prostatic adenocarcinoma, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp. 461–468 (2013)

  10. LeCun, Y., Kavukcuoglu, K., Farabet, C.: Convolutional networks and applications in vision. In: Proceedings of 2010 IEEE International Symposium on Circuits and Systems. IEEE, pp. 253–256 (2010)

  11. Cireşan, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Mitosis detection in breast cancer histology images with deep neural networks. In: International Conference on Medical Image Computing and Computer-assisted Intervention. Springer, pp. 411–418 (2013)

  12. Pang, B., Zhang, Y., Chen, Q., Gao, Z., Peng, Q., You, X.: Cell nucleus segmentation in color histopathological imagery using convolutional networks. In: 2010 Chinese Conference on Pattern Recognition (CCPR). IEEE, pp. 1–5 (2010)

  13. Malon, C.D., Cosatto, E.: Classification of mitotic figures with convolutional neural networks and seeded blob features. J. Pathol. Inform. 4, 9 (2013)

    Google Scholar 

  14. Habibzadeh, M., Krzyżak, A., Fevens, T.: White blood cell differential counts using convolutional neural networks for low resolution images. In: International Conference on Artificial Intelligence and Soft Computing. Springer, pp. 263–274 (2013)

  15. Sim, K., Chia, F., Chong, S., Tso, C., Abbas, S.F.: Real time based computer-aided design MRI breast cancer detection and data management system. In: Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV), The Steering Committee of The World Congress in Computer Science, p. 1 (2011)

  16. Giger, M.L., Karssemeijer, N., Schnabel, J.A.: Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer. Annu. Rev. Biomed. Eng. 15, 327–357 (2013)

    Google Scholar 

  17. Cheng, H.-D., Shi, X., Min, R., Hu, L., Cai, X., Du, H.: Approaches for automated detection and classification of masses in mammograms. Pattern Recogn. 39(4), 646–668 (2006)

    Google Scholar 

  18. Wei, L., Yang, Y., Nishikawa, R.M., Jiang, Y.: A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications. IEEE Trans. Med. Imaging 24(3), 371–380 (2005)

    Google Scholar 

  19. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

  20. Bengio, Y., et al.: Learning deep architectures for ai, Foundations and trends®. Mach. Learn. 2(1), 1–127 (2009)

    MATH  Google Scholar 

  21. Zou, W., Zhu, S., Yu, K., Ng, A.Y.: Deep learning of invariant features via simulated fixations in video. In: Advances in Neural Information Processing Systems, pp. 3203–3211 (2012)

  22. Carneiro, G., Nascimento, J.C.: Combining multiple dynamic models and deep learning architectures for tracking the left ventricle endocardium in ultrasound data. IEEE Trans. Pattern Anal. Mach. Intell. 35(11), 2592–2607 (2013)

    Google Scholar 

  23. Li, R., Zhang, W., Suk, H.-I., Wang, L., Li, J., Shen, D., Ji, S.: Deep learning based imaging data completion for improved brain disease diagnosis. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, pp. 305–312 (2014)

  24. Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems, pp. 3320–3328 (2014)

  25. Roth, H.R., Lu, L., Seff, A., Cherry, K.M., Hoffman, J., Wang, S., Liu, J., Turkbey, E., Summers, R.M.: A new 2.5 d representation for lymph node detection using random sets of deep convolutional neural network observations. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, pp. 520–527 (2014)

  26. Brosch, T., Yoo, Y., Li, D.K., Traboulsee, A., Tam, R.: Modeling the variability in brain morphology and lesion distribution in multiple sclerosis by deep learning. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, pp. 462–469 (2014)

  27. Guo, Y., Wu, G., Commander, L.A., Szary, S., Jewells, V., Lin, W., Shen, D.: Segmenting hippocampus from infant brains by sparse patch matching with deep-learned features. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, pp. 308–315 (2014)

  28. Mercan, C., Aksoy, S., Mercan, E., Shapiro, L.G., Weaver, D.L., Elmore, J.G.: Multi-instance multi-label learning for multi-class classification of whole slide breast histopathology images. IEEE Trans. Med. Imaging 37(1), 316–325 (2017)

    Google Scholar 

  29. Huang, Q., Chen, Y., Liu, L., Tao, D., Li, X.: On combining biclustering mining and adaboost for breast tumor classification. IEEE Trans. Knowl. Data Eng. 32, 728–738 (2019)

    Google Scholar 

  30. Zhou, L., Zhang, Z., Chen, Y.-C., Zhao, Z.-Y., Yin, X.-D., Jiang, H.-B.: A deep learning-based radiomics model for differentiating benign and malignant renal tumors. Transl. Oncol. 12(2), 292–300 (2019)

    Google Scholar 

  31. Han, S.S., Kim, M.S., Lim, W., Park, G.H., Park, I., Chang, S.E.: Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm. J. Investig. Dermatol. 138(7), 1529–1538 (2018)

    Google Scholar 

  32. Janowczyk, A., Madabhushi, A.: Deep learning for digital pathology image analysis: a comprehensive tutorial with selected use cases. J. Pathol. Inform. (2016). https://doi.org/10.4103/2153-3539.186902

    Article  Google Scholar 

  33. Wang, S.-H., Zhang, Y.-D., Yang, M., Liu, B., Ramirez, J., Gorriz, J.M.: Unilateral sensorineural hearing loss identification based on double-density dual-tree complex wavelet transform and multinomial logistic regression. Integr. Comput.-Aided Eng. 26(4), 411–426 (2019)

    Google Scholar 

  34. Ho, T.K.: Random decision forests. In: Proceedings of 3rd International Conference on Document Analysis and Recognition, vol. 1. IEEE, pp. 278–282 (1995)

  35. Fernández-Delgado, M., Cernadas, E., Barro, S., Amorim, D.: Do we need hundreds of classifiers to solve real world classification problems? J. Mach. Learn. Res. 15(1), 3133–3181 (2014)

    MathSciNet  MATH  Google Scholar 

  36. Géron, A.: Hands-on machine learning with Scikit–Learn, Keras, and TensorFlow: concepts, tools, and techniques to build intelligent systems. O’Reilly Media (2019)

  37. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)

    MathSciNet  MATH  Google Scholar 

  38. Wehrens, R., Putter, H., Buydens, L.M.: The bootstrap: a tutorial. Chemometr. Intell. Lab. Syst. 54(1), 35–52 (2000)

    Google Scholar 

  39. Breiman, L.: Pasting small votes for classification in large databases and on-line. Mach. Learn. 36(1–2), 85–103 (1999)

    Google Scholar 

  40. Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)

    MATH  Google Scholar 

  41. Wang, S.-H., Muhammad, K., Hong, J., Sangaiah, A.K., Zhang, Y.-D.: Alcoholism identification via convolutional neural network based on parametric relu, dropout, and batch normalization. Neural Comput. Appl. 32(3), 665–680 (2020)

    Google Scholar 

Download references

Acknowledgements

Thanks to CSIR for giving me a senior research fellowship (SRF) by the grace of which I am capable of doing research and writing this paper. The authors also thank all friends, teachers, and relatives who helped me to write this paper and provide such an environment; without their motivation and help, it was impossible.

Author information

Authors and Affiliations

  1. School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, 110067, India

    Yadavendra & Satish Chand

Authors
  1. Yadavendra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Satish Chand
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yadavendra.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yadavendra, Chand, S. A comparative study of breast cancer tumor classification by classical machine learning methods and deep learning method. Machine Vision and Applications 31, 46 (2020). https://doi.org/10.1007/s00138-020-01094-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00138-020-01094-1

Keywords

Search

Navigation