Skip to main content
SpringerLink
Log in

Comparison of Deep Feature Classification and Fine Tuning for Breast Cancer Histopathology Image Classification

  • Conference paper
  • First Online:
Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1036))

Abstract

Convolutional Neural Networks (ConvNets) are increasingly being used for medical image diagnostic applications. In this paper, we compare two transfer learning approaches - Deep Feature classification and Fine-tuning ConvNets for Diagnosing Breast Cancer malignancy. BreaKHis dataset is used to benchmark our results with ResNet-50, InceptionV2 and DenseNet-169 pre-trained models. Deep feature classification accuracy ranges from 81% to 95% using Logistic Regression, LightGBM and Random Forest classifiers. Fine-tuned DenseNet-169 model accuracy outperformed all other classification models with 99.25 ± 0.4%.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Veta, M., Pluim, J., Van Deist, P.J., Viergever, M.A.: Breast cancer histopathology image analysis: a review. IEEE Trans. Biomed. Eng. 61(5), 1400–1411 (2014). https://doi.org/10.1109/TBME.2014.2303852

    Article  Google Scholar 

  2. U.S. Breast Cancer statistics. http://www.breastcancer.org/symptoms/understand_bc/statistics

  3. Elmore, J.G., Longton, G.M., Carney, P.A., Geller, B.M., Onega, T., Tosteson, A.N.A., et al.: Diagnostic concordance among pathologists interpreting breast biopsy specimens. J. Am. Med. Assoc. 313, 1122–1132 (2015). https://doi.org/10.1001/jama.2015.1405

    Article  Google Scholar 

  4. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceedings of 26th Annual Conference on Neural Information Processing Systems (NIPS), December 2012, pp. 1106–1114 (2012)

    Google Scholar 

  5. Spanhol, F.A., Oliveira, L.S., et al.: Breast cancer histopathological image classification using convolutional neural networks. In: Proceedings of 2016 IEEE International Joint Conference on Neural Network, July 2016, pp. 2560–2567. IEEE (2016). https://doi.org/10.1109/ijcnn.2016.7727519

  6. Abu Samah, A., Fauzi, M.F.A., Mansor, S.: Classification of benign and malignant tumors in histopathology images. In: Proceedings of 2017 IEEE International Conference on Signal and Image Processing Application, September 2017, pp. 43–48 (2017). https://doi.org/10.1109/icsipa.2017.8120587

  7. Sun, J., Binder, A.: Comparison of deep learning architectures for H&E histopathology images. In: Proceedings of 2017 IEEE Conference on Big Data and Analytics, November 2017. https://doi.org/10.1109/icbdaa.2017.8284105

  8. Bayramoglu, N., Kannala, J., Heikkila, J.: Deep learning for magnification independent breast cancer histopathology image classification. In: 23rd International Conference on Pattern Recognition, pp. 2440–2445 (2016). https://doi.org/10.1109/icpr.2016.7900002

  9. Santosh, K.C., Vajda, S., Antani, S.: Edge map analysis in chest X-rays for automatic pulomary abnormality screening. JCARS 11, 1637 (2016). https://doi.org/10.1007/s11548-016-1359-6

    Article  Google Scholar 

  10. Dhandra, B.V., Hegadi, R.: Classification of abnormal endoscopic images using morphological watershed segmentation. In: International Conference on Cognition and Recognition, (ICCR-2005), Mysore, India, 22–23 December 2005, pp. 695–700 (2005). ISBN 81-7764-952-3

    Google Scholar 

  11. Dhandra, B.V., Hegadi, R.: Active contours without edges and curvature analysis for endoscopic image classification. Int. J. Comput. Sci. Secur. 1(1), 19–32 (2007)

    Google Scholar 

  12. Hiremath, P.S., Dhandra, B.V., Humnabad, I., Hegadi, R., Rajput, G.G.: Detection of esophageal Cancer (Necrosis) in the Endoscopic images using color image segmentation. In: Second National Conference on Document Analysis and Recognition (NCDAR-2003), Mandya, India, pp. 417–422 (2003)

    Google Scholar 

  13. Nahid, A., Kong, Y.: Local and global feature utilization for breast image classification by convolutional neural network. In: 2017 International Conference on Digital Image Computing: Techniques and Applications, NSW, pp. 1–6 (2017). https://doi.org/10.1109/dicta.2017.8227460

  14. Wan, S., Huang, X., Lee, H.C., Fujimoto, J.G., Zhou, C.: Spoke-LBP and ring-LBP: new texture features for tissue classification. In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), pp. 195–199. IEEE (2015). https://doi.org/10.1109/isbi.2015.7163848

  15. Vajda, S., Karargyris, A., Jaeger, S., et al.: Feature selection for automatic tuberculosis screening in frontal chest radiograph. J. Med. Syst. 42, 146 (2018). https://doi.org/10.1007/s10916-018-0991-9

    Article  Google Scholar 

  16. Santosh, K.C., Antani, S.: Automated chest x-ray screening: can lung region symmetry help detect pulmonary abnormalities. IEEE Trans. Med. Imaging 37(5), 1168–1177 (2018). https://doi.org/10.1109/TMI.2017.2775636

    Article  Google Scholar 

  17. Karargyris, A., Siegelman, J., Tzortzis, D., Jaeger, S., et al.: Combination of texture and shape features to detect pulmonary abnormalities in digital chest X-rays. Int. J. Comput. Assist. Radiol. Surg. 11, 99 (2016). https://doi.org/10.1007/s11548-015-1242-x

    Article  Google Scholar 

  18. Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: delving deep into convolutional nets. arXiv preprint arXiv:1405.3531 (2014)

  19. Li, X., Pang, T., Xiong, B., Liu, W., Liang, P., Wang, T.: Convolutional neural networks based transfer learning for diabetic retinopathy fundus image classification. In: 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Shanghai, pp. 1–11 (2017). https://doi.org/10.1109/cisp-bmei.2017.8301998

  20. Niu, X.-X., Suen, C.Y.: A novel hybrid CNN–SVM classifier for recognizing handwritten digits. Pattern Recogn. 45(4), 1318–1325 (2012). https://doi.org/10.1016/j.patcog.2011.09.021

    Article  Google Scholar 

  21. Ukil, S., Ghosh, S., Obaidullah, S.M., Santosh, K.C., et al.: Deep learning for word-level handwritten Indic script identification. arXiv preprint arXiv:1801.01627 (2018)

  22. Babenko, A., Lempitsky, V.: Aggregating local deep features for image retrieval. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1269–1277 (2015). https://doi.org/10.1109/iccv.2015.150

  23. Alzu’bi, A., Amira, A., Ramzan, N.: Content-based image retrieval with compact deep convolutional features. Neurocomputing 249(2), 95–105 (2017). https://doi.org/10.1016/j.neucom.2017.03.072

    Article  Google Scholar 

  24. Penatti, O.A, Nogueira, K., dos Santos, J.A.: Do deep features generalize from everyday objects to remote sensing and aerial scenes domains. In: Computer Vision and Pattern Recognition Workshop. IEEE (2015). https://doi.org/10.1109/cvprw.2015.7301382

  25. Charan, S., Khan, M.J., Khurshid, K.: Breast cancer detection in mammograms using convolutional neural network. In: 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), Sukkur, pp. 1–5 (2018). https://doi.org/10.1109/icomet.2018.8346384

  26. Jiao, Z., Gao, X., Wang, Y., Li, J.: A deep feature based framework for breast masses classification. Neurocomputing 12, 221–231 (2016). https://doi.org/10.1016/j.neucom.2016.02.060

    Article  Google Scholar 

  27. ICIAR 2018 Grand Challenge on Breast Cancer Histology (BACH) images. https://iciar2018-challenge.grand-challenge.org/

  28. Rakhlin, A., Shvets, A., Iglovikov, V., Kalinin, A.A.: Deep convolutional neural networks for breast cancer histology image analysis. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds.) ICIAR 2018. LNCS, vol. 10882, pp. 737–744. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93000-8_83

    Chapter  Google Scholar 

  29. Habibzadeh, M., Motlagh, N.H., Jannesary, M., Aboulkheyr, H., Khosravi, P.: Breast cancer histopathological image classification: a deep learning approach. bioRxiv 242818 (2018). https://doi.org/10.1101/242818

  30. Han, Z., Wei, B., Zheng, Y., Yin, Y., Li, K., Li, S.: Breast cancer multi-classification from histopathological images with structured deep learning model. Sci. Rep. 7, 4172 (2017). https://doi.org/10.1038/s41598-017-04075-z

    Article  Google Scholar 

  31. Spanhol, F., Oliveira, L.S., Petitjean, C., Heutte, L.: A dataset for breast cancer histopathological image classification. IEEE Trans. Biomed. Eng. 63(7), 1455–1462 (2016). https://doi.org/10.1109/tbme.2015.2496264

    Article  Google Scholar 

  32. Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., Lew, M.S.: Deep learning for visual understanding: a review. Neurocomputing 187, 27–48 (2016). https://doi.org/10.1016/j.neucom.2015.09.116

    Article  Google Scholar 

  33. Huang,G., Liu, Z., van der Maaten, L.: Densely connected convolutional network. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2261–2269 (2017). https://doi.org/10.1109/cvpr.2017.243

  34. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016). https://doi.org/10.1109/cvpr.2016.90

  35. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)

    Google Scholar 

  36. Keras: The Python Deep Learning Library. https://keras.io/

  37. Haifley, T.: Linear logistic regression: an introduction. In: IEEE International Integrated Reliability Workshop Final Report, pp. 184–187 (2002). https://doi.org/10.1109/irws.2002.1194264

  38. Ke, G., et al.: LightGBM: a highly efficient gradient boosting decision tree. In: Advances in Neural Information Processing Systems, pp. 3149–3157 (2017)

    Google Scholar 

  39. Breiman, L.: Random forests. J. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/a:1010933404324

    Article  MATH  Google Scholar 

  40. 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)

    Google Scholar 

  41. Tajbakhsh, N., et al.: Convolutional neural networks for medical image analysis: full training or fine tuning. IEEE Trans. Med. Imaging 35(5), 1299–1312 (2016). https://doi.org/10.1109/tmi.2016.2535302

    Article  Google Scholar 

  42. Hu, F., Xia, G.-S., Hu, J., Zhang, L.: Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. MDPI Remote Sens. 7(11), 14680–14707 (2015). https://doi.org/10.3390/rs71114680

    Article  Google Scholar 

  43. NVIDIA Corporation: Nvidia tesla product literature (2018). https://www.nvidia.com/en-us/design-visualization/quadro-desktop-gpus/

  44. Hegadi, R.S., Navale, D.I., Pawar, T.D., Ruikar, D.D.: Multi feature-based classification of osteoarthritis in knee joint X-ray images, Chap. 5. In: Medical Imaging: Artificial Intelligence, Image Recognition, and Machine Learning Techniques. CRC Press (2019). ISBN 9780367139612

    Google Scholar 

  45. Ruikar, D.D., Santosh, K.C., Hegadi, R.S.: Automated fractured bone segmentation and labeling from CT images. J. Med. Syst. 43(3), 60 (2019). https://doi.org/10.1007/s10916-019-1176-x

    Article  Google Scholar 

  46. Ruikar, D.D., Santosh, K.C., Hegadi, R.S.: Segmentation and analysis of CT images for bone fracture detection and labeling, Chap. 7. In: Medical Imaging: Artificial Intelligence, Image Recognition, and Machine Learning Techniques. CRC Press (2019). ISBN 9780367139612

    Google Scholar 

  47. Ruikar, D.D., Hegadi, R.S., Santosh, K.C.: A systematic review on orthopedic simulators for psycho-motor skill and surgical procedure training. J. Med. Syst. 42(9), 168 (2018)

    Article  Google Scholar 

  48. Sawat, D.D., Hegadi, R.S.: Unconstrained face detection: a Deep learning and Machine learning combined approach. CSI Trans. ICT 5(2), 195–199 (2017)

    Article  Google Scholar 

  49. Jagtap, A.B., Hegadi, R.S.: Feature learning for offline handwritten signature verification using Convolution Neural Network. Int. J. Technol. Hum. Interact. (IJTHI). ISSN 1548-3908

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Cognizant Technology Solutions India Private Ltd., Chennai, India

    D. Sabari Nathan, R. Saravanan, J. Anbazhagan & Praveen Koduganty

Authors
  1. D. Sabari Nathan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Saravanan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J. Anbazhagan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Praveen Koduganty
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. Saravanan .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of South Dakota, Vermillion, SD, USA

    K. C. Santosh

  2. Solapur University, Solapur, India

    Ravindra S. Hegadi

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sabari Nathan, D., Saravanan, R., Anbazhagan, J., Koduganty, P. (2019). Comparison of Deep Feature Classification and Fine Tuning for Breast Cancer Histopathology Image Classification. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1036. Springer, Singapore. https://doi.org/10.1007/978-981-13-9184-2_5

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-9184-2_5

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9183-5

  • Online ISBN: 978-981-13-9184-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation