Skip to main content
SpringerLink
Log in

Entropy Role on Patch-Based Binary Classification for Skin Melanoma

  • Conference paper
  • First Online:
Advances in Computational Collective Intelligence (ICCCI 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1463))

Included in the following conference series:

Abstract

In this paper, we split the region of interest of dermoscopic images of skin lesions in patches of different size and we analyze the impact of the entropy of the patches on patch-based binary classification using a convolutional neural network (CNN). Specifically, we analyze the distribution of entropy amongst the patches and we compare the training time of a classifier on subsets of the data with varying entropy. We find that the classifier converges faster on patches with higher entropy. Our entropy-based analysis is performed on skin lesion images from the ISIC archive.

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 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
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

Notes

  1. 1.

    The data is publicly available at https://www.isic-archive.com.

  2. 2.

    https://isic-archive.com/api/v1.

  3. 3.

    Originally, the ISIC challenge had more refined categories. In this paper we use only 2.

References

  1. Anwar, S.M., Majid, M., Qayyum, A., Awais, M., Alnowami, M., Khan, M.K.: Medical image analysis using convolutional neural networks: a review. J. Med. Syst. 42(11), 1–13 (2018). https://doi.org/10.1007/s10916-018-1088-1

    Article  Google Scholar 

  2. Esteva, A., Kuprel, B., Novoa, R.A., Ko, J., Swetter, S.M., Blau, H.M., Thrun, S.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115–118 (2017). https://doi.org/10.1038/nature21056

    Article  Google Scholar 

  3. Favole, F., Trocan, M., Yilmaz, E.: Melanoma detection using deep learning. In: Nguyen, N.T., Hoang, B.H., Huynh, C.P., Hwang, D., Trawiński, B., Vossen, G. (eds.) ICCCI 2020. LNCS (LNAI), vol. 12496, pp. 816–824. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-63007-2_64

    Chapter  Google Scholar 

  4. Gessert, N., Nielsen, M., Shaikh, M., Werner, R., Schlaefer, A.: Skin lesion classification using ensembles of multi-resolution EfficientNets with meta data. MethodsX 7, 100864 (2020). https://doi.org/10.1016/j.mex.2020.100864

    Article  Google Scholar 

  5. Gessert, N., et al.: Skin lesion classification using CNNs with patch-based attention and diagnosis-guided loss weighting. IEEE Trans. Biomed. Eng. 67(2), 495–503 (2020). https://doi.org/10.1109/TBME.2019.2915839

    Article  Google Scholar 

  6. Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Teh, Y.W., Titterington, D.M. (eds.) Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2010, Chia Laguna Resort, Sardinia, Italy, 13–15May 2010. JMLR Proceedings, vol. 9, pp. 249–256. JMLR.org (2010)

    Google Scholar 

  7. Gutman, D., et al.: Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC). arXiv:1605.01397 [cs] (May 2016)

  8. He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 770–778. IEEE (2016). https://doi.org/10.1109/CVPR.2016.90

  9. Hou, L., Samaras, D., Kurc, T.M., Gao, Y., Davis, J.E., Saltz, J.H.: Patch-based convolutional neural network for whole slide tissue image classification. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2424–2433 (2016). https://doi.org/10.1109/CVPR.2016.266

  10. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR abs/1412.6980 (2015)

    Google Scholar 

  11. Marentakis, P., et al.: Lung cancer histology classification from CT images based on radiomics and deep learning models. Med. Biol. Eng. Comput. 59(1), 215–226 (2021). https://doi.org/10.1007/s11517-020-02302-w

    Article  Google Scholar 

  12. Nigam, K., Lafferty, J., McCallum, A.: Using maximum entropy for text classification. In: IJCAI-99 Workshop on Machine Learning for Information Filtering, Stockholom, Sweden, vol. 1, pp. 61–67 (1999)

    Google Scholar 

  13. Ouchtati, S., Chergui, A., Mavromatis, S., Aissa, B., Rafik, D., Sequeira, J.: Novel method for brain tumor classification based on use of image entropy and seven hu’s invariant moments. Traitement du Signal 36(6), 483–491 (2019). https://doi.org/10.18280/ts.360602

  14. Ritchie, H., Roser, M.: Causes of death. In: Our World in Data (2018)

    Google Scholar 

  15. Rotemberg, V., et al.: A patient-centric dataset of images and metadata for identifying melanomas using clinical context. Sci. Data 8(1), 34 (2021). https://doi.org/10.1038/s41597-021-00815-z

    Article  Google Scholar 

  16. Rousseau, F., Habas, P.A., Studholme, C.: A supervised patch-based approach for human brain labeling. IEEE Trans. Med. Imaging 30(10), 1852–1862 (2011). https://doi.org/10.1109/TMI.2011.2156806

    Article  Google Scholar 

  17. Roy, K., Banik, D., Bhattacharjee, D., Nasipuri, M.: Patch-based system for classification of Breast Histology images using deep learning. Comput. Med. Imaging Graph. 71, 90–103 (2019). https://doi.org/10.1016/j.compmedimag.2018.11.003

    Article  Google Scholar 

  18. Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27(3), 379–423 (1948). https://doi.org/10.1002/j.1538-7305.1948.tb01338.x

    Article  MathSciNet  MATH  Google Scholar 

  19. Siegel, R.L., Miller, K.D., Fuchs, H.E., Jemal, A.: Cancer statistics. CA Cancer J. Clin. 71(1), 7–33 (2021). https://doi.org/10.3322/caac.21654

  20. Skilling, J., Bryan, R.: Maximum entropy image reconstruction-general algorithm. Mon. Not. R. Astron. Soc. 211, 111 (1984)

    Article  Google Scholar 

  21. Tschandl, P., Rosendahl, C., Kittler, H.: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5(1), 180161 (2018). https://doi.org/10.1038/sdata.2018.161

    Article  Google Scholar 

  22. Yala, A., Lehman, C., Schuster, T., Portnoi, T., Barzilay, R.: A deep learning mammography-based model for improved breast cancer risk prediction. Radiology 292(1), 60–66 (2019). https://doi.org/10.1148/radiol.2019182716

    Article  Google Scholar 

  23. Yilmaz, E., Trocan, M.: Benign and malignant skin lesion classification comparison for three deep-learning architectures. In: Nguyen, N.T., Jearanaitanakij, K., Selamat, A., Trawiński, B., Chittayasothorn, S. (eds.) ACIIDS 2020. LNCS (LNAI), vol. 12033, pp. 514–524. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-41964-6_44

    Chapter  Google Scholar 

  24. Zhang, F., et al.: Lung nodule classification with multilevel patch-based context analysis. IEEE Trans. Biomed. Eng. 61(4), 1155–1166 (2014). https://doi.org/10.1109/TBME.2013.2295593

    Article  Google Scholar 

  25. Zhu, S.C., Wu, Y.N., Mumford, D.: Minimax entropy principle and its application to texture modeling. Neural Comput. 9(8), 1627–1660 (1997). https://doi.org/10.1162/neco.1997.9.8.1627

Download references

Author information

Authors and Affiliations

  1. ISEP - Institut Supérieur d’Électronique de Paris, 10 rue de Vanves, Issy les Moulineaux, 92130, France

    Guillaume Lachaud, Patricia Conde-Cespedes & Maria Trocan

Authors
  1. Guillaume Lachaud
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Patricia Conde-Cespedes
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maria Trocan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Guillaume Lachaud , Patricia Conde-Cespedes or Maria Trocan .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Krystian Wojtkiewicz

  2. VU Amsterdam, Amsterdam, The Netherlands

    Jan Treur

  3. University of the West of England, Bristol, UK

    Elias Pimenidis

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Marcin Maleszka

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lachaud, G., Conde-Cespedes, P., Trocan, M. (2021). Entropy Role on Patch-Based Binary Classification for Skin Melanoma. In: Wojtkiewicz, K., Treur, J., Pimenidis, E., Maleszka, M. (eds) Advances in Computational Collective Intelligence. ICCCI 2021. Communications in Computer and Information Science, vol 1463. Springer, Cham. https://doi.org/10.1007/978-3-030-88113-9_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-88113-9_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88112-2

  • Online ISBN: 978-3-030-88113-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation