Classifying Breast Cancer Using Deep Convolutional Neural Network Method

Author:

Rahman MusfequaORCID,Deb KaushikORCID,Jo Kang-HyunORCID

Publisher

Springer Nature Singapore

Reference24 articles.

1. International Agency for Research on Cancer: “World Fact Sheet” (2020). https://gco.iarc.fr/today/data/factsheets/populations/900-world-fact-sheets.pdf/. Accessed 26 June 2022

2. World Health Organization (WHO): “20-Breast-fact-sheet” (2020). https://gco.iarc.fr/404. Accessed 26 June 2022

3. Karthiga, R., Narasimhan, K.: Automated diagnosis of breast cancer using wavelet based entropy features. In: 2018 2nd International Conference on Electronics, Communication and Aerospace Technology (ICECA), pp. 274–279. IEEE (2018)

4. Horvat, J.V., Keating, D.M., Rodrigues-Duarte, H., Morris, E.A., Mango, V.L.: Calcifications at digital breast tomosynthesis: imaging features and biopsy techniques. Radiographics 39(2), 307–318 (2019)

5. Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights Imaging 9, 611–629 (2018). https://doi.org/10.1007/s13244-018-0639-9

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