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Klasifikasi Citra Histopatologi Kanker Payudara menggunakan Data Resampling Random dan Residual Network

*Wahyudi Setiawan  -  Universitas Trunojoyo Madura, Indonesia

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Abstract

Data imbalance between classes is one of the problems on image classification. The data in each class is not equal and has a relatively large difference generated in less than optimal classification results. Ideally, the data in each class is equal or have a slight difference. This article discusses the classification of the histopathology breast cancer image. The data consist of  8 classes with unbalanced data. The method for balancing the data in each class uses random resampling which is applied to training data only. The data used from BreakHist with magnifications of 40x, 100x, 200x, and 400x . The classification uses Residual Network (ResNet) 18 and 50. The best results are obtained on images with a magnification of 400x. Classification results using ResNet18 has an average accuracy of 79.82%, an average precision of 71.39%, and an average recall of 82.37%. Meanwhile using ResNet50 showed an average accuracy of 81.67%, average precision of 78.41%, and an average recall of 82.91%.

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Keywords: Histopathology; Breast Cancer; Random Resampling Data; Residual Network; Image Classification

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  1. American Cancer Society, 2019. Breast Cancer Facts & Figures 2019-2020
  2. Chawla, N.V, Bowyer, K. W., & Hall, L. O., 2002. SMOTE : Synthetic Minority Over-sampling Technique. Journal of Artifial Intelligence Research, 16, 321–357
  3. Cruz-roa, A., Basavanhally, A., Gonz, F., Gilmore, H., & Feldman, M., 2014. Automatic detection of invasive ductal carcinoma in whole slide images with Convolutional Neural Networks. In Medical Imaging 2014 : Digital Pathology (Vol. 9041, pp. 1–15). https://doi.org/10.1117/12.2043872
  4. Dumitru, A., Procop, A., Iliesiu, A., Tampa, M., Mitrache, L., Costache, M., … Cirstoiu, M., 2015. Mucinous Breast Cancer: a Review Study of 5 Year Experience from a Hospital-Based Series of Cases. Medica, 10(1), 14–18
  5. Elston, C. W., & Ellis, I. O., 2002. Pathological prognostic factors in breast cancer . I . The value of histological grade in breast cancer : experience from a large. Histopathology, 41, 151–153
  6. Gurcan, M. N., Boucheron, L., Can, Al., Madabhushi, A., Rajpoot, N., & Yener, B., 2009. Histopathological Image ANalysis : A Review. IEEE Rev Biomed Eng, 2, 147–171. https://doi.org/10.1109/RBME.2009.2034865.Histopathological
  7. 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. Scientific Reports, 7(May), 1–10. https://doi.org/10.1038/s41598-017-04075-z
  8. He, H., & Garcia, E. A., 2009. Learning from Imbalanced Data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263–1284
  9. He, K., & Sun, J., 2015. Deep Residual Learning for Image Recognition. IEEE Xplore, 1–9. Retrieved from https://arxiv.org/abs/1512.03385
  10. He, K., Zhang, X., Ren, S., & Sun, J., 2015. Delving Deep into Rectifiers : Surpassing Human-Level Performance on ImageNet Classification. Retrieved from https://arxiv.org/abs/1502.01852
  11. Hilbertina, N., 2015. Peranan patologi dalam diagnostik tumor payudara. Majalah Kedokteran Andalas, 38(1), 1–8
  12. Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Adam, H., 2017. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Andrew
  13. Janowczyk, A., & Madabhushi, A., 2016. Deep learning for digital pathology image analysis : A comprehensive tutorial with selected use cases. J Pathol Inform, 7(29), 1–18. https://doi.org/10.4103/2153-3539.186902
  14. Kementrian Kesehatan RI, 2016. infodatin kanker payudara 2016.pdf
  15. Koziarski, M., 2020. Two-Stage Resampling for Convolutional Neural Network Training in the Imbalanced Colorectal Cancer Image Classification, 1–15
  16. Kuijper, A., Mommers, E. C. M., Wall, E. Van Der, & Diest, P. J. Van, 2001. Histopathology of Fibroadenoma of the Breast. Anatomic Pathology, 115, 736–742
  17. Kumar, A., Kumar, S., Saxena, S., & Lakshmanan, K., 2020. Deep feature learning for histopathological image classification of canine mammary tumors and human breast cancer. Information Sciences, 508, 405–421. https://doi.org/10.1016/j.ins.2019.08.072
  18. Matos, J. De, Jr, A. D. S. B., Oliveira, L. E. S., & Koerich, A. L., 2019. Double Transfer Learning for Breast Cancer Histopathologic Image Classification. 2019 International Joint Conference on Neural Networks (IJCNN), (July), 1–8
  19. Reza, S., & Ma, J., 2018. Imbalanced Histopathological Breast Cancer Image Classification with Convolutional Neural Network. 2018 14th IEEE International Conference on Signal Processing (ICSP), 619–624
  20. Spanhol, F. A., Oliveira, L. S., Petitjean, C., & Heutte, L., 2016. A Dataset for Breast Cancer Histopathological Image Classification. IEEE Transactions on Biomedical Engineering, 63(7), 1455–1462
  21. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., … Arbor, A., 2015. Going Deeper with Convolutions. In CVPR2015 (pp. 1–9). https://doi.org/10.1109/CVPR.2015.7298594
  22. Theodoridis, S., & Koutroumbas, K., 2003. Pattern Recognition (Second Edi). San Diego, California: Elsevier
  23. World Health Organization, 2014. WHO Position paper on mammography screening
  24. Zakaria, F., & Ahmed, S., 2016. Histological variations in fibroadenoma of breast. Histological Variations in Fibroadenoma of Breast, 7(11), 760–764. https://doi.org/10.7439/ijbr

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