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International Conference on Soft Computing and Pattern Recognition

SoCPaR 2019: Proceedings of the 11th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2019) pp 226–234Cite as

Computer Aided System for Nuclei Localization in Histopathological Images Using CNN

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1182))

Abstract

Today, the health care industry is extensively using computer aided diagnostic expert system. The expert system for the diagnosis of breast cancer using the histopathological image is the need of time. Analysis of histopathological images is challenging due to its complex architecture with irregularly shaped nuclei. Convolutional neural network (CNN) is a promising technology emerging in recent years. We have designed a computer based expert system to identify nuclei in histopathological images. The system is developed using python programming language. We have used the BreaKHis breast cancer dataset for experimentation and Kaggle dataset for convolution masks generation. Nucleases are localized using custom design Keras and U-Net Hybrid CNN (KUH-CNN) model. The systems can be used by histopathologists for the diagnosis of malignancy in the tissue. The system can also aid the researchers who can implement a machine learning algorithm on the nucleases detected images for further analysis.

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References

  1. Zeng, Z., Xie, W., Zhang, Y., Lu, Y.: RIC-Unet: an improved neural network based on Unet for nuclei segmentation in histology images. IEEE Access 7, 21420–21428 (2019)

    Article  Google Scholar 

  2. Yousefi, S., Nie, Y.: Transfer learning from nucleus detection to classification in histopathology images. In: 16th International Symposium on Biomedical Imaging, Venice, Italy, vol. 16, pp. 957–960 (2019)

    Google Scholar 

  3. Tofighi, M., Guo, T., Vanamala, J.K.P., Monga, V.: Prior information guided regularized deep learning for cell nucleus detection. IEEE Trans. Med. Imaging 38, 2047–2058 (2019)

    Article  Google Scholar 

  4. Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.: Breast cancer histopathological image classification using convolutional neural networks. In: International Joint Conference on Neural Networks (IJCNN). Vancouver, BC, pp. 2560–2567 (2016)

    Google Scholar 

  5. Ye, J., Luo, Y., Zhu, C., Liu, F., Zhang, Y.: Breast cancer image classification on WSI with spatial correlations. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, pp. 1219–1223 (2019)

    Google Scholar 

  6. Kumar, K., Rao, A.C.S.: Breast cancer classification of image using convolutional neural networks. In: 4th International Conference on Recent Advances in Information Technology (RAIT), Dhanbad, India (2018)

    Google Scholar 

  7. Angara, S., Robinson, M., Guillen-Rondon, P.: Convolutional neural networks for breast cancer histopathological image classification. In: 4th International Conference on Big Data and Information Analytics (BigDIA), Houston, TX, USA (2018)

    Google Scholar 

  8. Jafarbiglo, S.K., Danyali, H., Helfroush, M.S.: Nuclear atypia grading in histopathological images of breast cancer using convolutional neural networks. In: 4th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS), Tehran, Iran, pp. 89–93 (2018)

    Google Scholar 

  9. He, S., Ruan, J., Long, Y., Wang, J., Wu, C., Ye, G., Zhang, Y.: Combining deep learning with traditional features for classification and segmentation of pathological images of breast cancer. In: 11th International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, China, pp. 3–6 (2018)

    Google Scholar 

  10. Chang, J., Yu, J., Han, T., Chang, H., Park, E.: A method for classifying medical images using transfer learning: A pilot study on histopathology of breast cancer. In: IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom), Dalian, China (2017)

    Google Scholar 

  11. Spanhol, F.A., Oliveira, L.S., Cavalin, P.R., Petitjean, C., Heutte, L.: Deep features for breast cancer histopathological image classification. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), Banff, AB, Canada, pp. 1868–1873 (2017)

    Google Scholar 

  12. Yan, R., Ren, F., Wang, Z., Wang, L., Ren, Y., Liu, Y., Zhang, F.: A hybrid convolutional and recurrent deep neural network for breast cancer pathological image classification. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid, Spain, pp. 957–962 (2018)

    Google Scholar 

  13. Garud, H., Karri, S.P.K., Sheet, D., Chatterjee, J., Mahadevappa, M., Ray, A.K., Maity, A.K.: High-magnification multi-views based classification of breast fine needle aspiration cytology cell samples using fusion of decisions from deep convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 828–833 (2017)

    Google Scholar 

  14. Nahid, A.-A., Ali, F.B., Kong, Y.: Histopathological breast-image classification with image enhancement by convolutional neural network. In: 20th International Conference of Computer and Information Technology (ICCIT), Dhaka, Bangladesh (2017)

    Google Scholar 

  15. Reza, M.S., Ma, J.: Imbalanced histopathological breast cancer image classification with convolutional neural network. In: 14th IEEE International Conference on Signal Processing (ICSP), Beijing, China, pp. 619–624 (2018)

    Google Scholar 

  16. Su, H., Liu, F., Xie, Y., Xing, F., Meyyappan, S., Yang, L.: Region segmentation in histopathological breast cancer images using deep convolutional neural network. In: IEEE 12th International Symposium on Biomedical Imaging (ISBI), New York, USA, pp. 55–58 (2015)

    Google Scholar 

  17. Kanojia, M.G., Abraham, S.: Breast cancer detection using RBF neural network. In: 2nd International Conference on Contemporary Computing and Informatics (IC3I), Noida, India, pp. 363–368 (2016)

    Google Scholar 

  18. Veta, M., Pluim, J.P.W., Diest, P.J.V., Viergever, M.A.: Breast cancer histopathology image analysis a review. IEEE Trans. Biomed. Eng. 61(5), 1400–1411 (2014)

    Article  Google Scholar 

  19. Ronneberger O., Fischer P., Brox T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI. LNCS, vol. 9351. Springer, Cham (2015)

    Google Scholar 

  20. Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.: A dataset for breast cancer histopathological image classification. IEEE Trans. Biomed. Eng. 63(7), 1–8 (2016)

    Article  Google Scholar 

  21. Stancin, I., Jovic, A.: An overview and comparison of free Python libraries for data mining and big data analysis. In: 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Hrvatska, pp. 1161–1166 (2019)

    Google Scholar 

  22. Wu, C., Ruan, J., Ye, G., Zhou, J., He, S., Wang, J., Zhang, Y.: Identifying tumor in whole-slide images of breast cancer using transfer learning and adaptive sampling. In: Eleventh International Conference on Advanced Computational Intelligence (ICACI), Guilin, China, pp. 167–172 (2019)

    Google Scholar 

  23. Sivaraman, K., Murthy, A.: Object recognition under lighting variations using pre-trained networks. In: IEEE Applied Imagery Pattern Recognition Workshop (AIPR), Washington, DC, USA. (2018)

    Google Scholar 

  24. Zhu, G., Li, B., Hong, S., Mao, B.: Texture recognition and classification based on deep learning. In: Sixth International Conference on Advanced Cloud and Big Data (CBD), Lanzhou, China (2018)

    Google Scholar 

  25. Kaggle. https://www.kaggle.com

  26. Keras: The Python deep learning library. https://keras.io

  27. Kunal, P., Mahendra, K., Brian, D., Niketa, G.: Breast cancer detection using WBCD. In: International Interdisciplinary Conference on Recent Trends in Science and Review of Research Journal. UGC Approved Journal No. 48514, Alibag, India, (2019)

    Google Scholar 

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Author information

Authors and Affiliations

  1. JJT University, Jhunjhunu, Rajasthan, India

    Mahendra G. Kanojia & S. K. Yadav

  2. SIES College of Commerce and Economics, Mumbai, Maharashtra, India

    Mohd. Abuzar Mohd. Haroon Ansari

  3. University of Mumbai, Mumbai, Maharashtra, India

    Niketa Gandhi

Authors
  1. Mahendra G. Kanojia
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  2. Mohd. Abuzar Mohd. Haroon Ansari
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  3. Niketa Gandhi
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  4. S. K. Yadav
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Corresponding author

Correspondence to Mahendra G. Kanojia .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR), Auburn, WA, USA

    Ajith Abraham

  2. Department of Computer Science and Engineering, Vardhaman College of Engineering, Hyderabad, Telangana, India

    M. A. Jabbar

  3. Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid, Spain

    Sanju Tiwari

  4. ISEP - Instituto Superior de Engenharia do Porto, Porto, Portugal

    Isabel M. S. Jesus

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© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

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Cite this paper

Kanojia, M.G., Ansari, M.A.M.H., Gandhi, N., Yadav, S.K. (2021). Computer Aided System for Nuclei Localization in Histopathological Images Using CNN. In: Abraham, A., Jabbar, M., Tiwari, S., Jesus, I. (eds) Proceedings of the 11th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2019). SoCPaR 2019. Advances in Intelligent Systems and Computing, vol 1182. Springer, Cham. https://doi.org/10.1007/978-3-030-49345-5_24

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