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2020 | OriginalPaper | Buchkapitel

Cancer Detection Based on Image Classification by Using Convolution Neural Network

verfasst von : Mohammad Anas Shah, Abdala Nour, Alioune Ngom, Luis Rueda

Erschienen in: Bioinformatics and Biomedical Engineering

Verlag: Springer International Publishing

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Abstract

Breast cancer starts when cells in the breast begin to grow out of control. These cells usually form a tumor that can often be seen on an x-ray or felt as a lump. The tumor is malignant (cancer) if the cells can grow into (invade) surrounding tissues or spread (metastasize) to distant areas of the body. The challenge of this project was to build an algorithm by using a neural network to automatically identify whether a patient is suffering from breast cancer by looking at biopsy images. The algorithm must be accurate because the lives of people are at stake.

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Literatur
1.
Zurück zum Zitat Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A., Jemal, A.: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA 68(6), 394–424 (2018)PubMed Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A., Jemal, A.: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA 68(6), 394–424 (2018)PubMed
2.
Zurück zum Zitat Ribli1, D., Anna, H., Zsuzsa, U., Peter, P., Istvan, C.: Detecting and classifying lesions in mammograms with Deep Learning. Sci. Rep. 4165(8), 2–6 (2018) Ribli1, D., Anna, H., Zsuzsa, U., Peter, P., Istvan, C.: Detecting and classifying lesions in mammograms with Deep Learning. Sci. Rep. 4165(8), 2–6 (2018)
3.
Zurück zum Zitat Kourou, K., Exarchos, T., Exarchos, K., Karamouzis, M., Fotiadis, D.: Machine learning applications in cancer prognosis and prediction. Comput. Struct. Biotechnol. J. 13(10), 1–2 (2014) Kourou, K., Exarchos, T., Exarchos, K., Karamouzis, M., Fotiadis, D.: Machine learning applications in cancer prognosis and prediction. Comput. Struct. Biotechnol. J. 13(10), 1–2 (2014)
4.
Zurück zum Zitat Spanhol, F., Oliveira, L.S., Petitjean, C., Heutte, L.: A dataset for breast cancer histopathological image classification. IEEE Trans. Biomed. Eng. (TBME) 63(7), 1455–1462 (2016)CrossRef Spanhol, F., Oliveira, L.S., Petitjean, C., Heutte, L.: A dataset for breast cancer histopathological image classification. IEEE Trans. Biomed. Eng. (TBME) 63(7), 1455–1462 (2016)CrossRef
5.
Zurück zum Zitat Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.: Breast cancer histopathological image classification using Convolutional Neural Networks. In: 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, pp. 2560–2567 (2016) Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.: Breast cancer histopathological image classification using Convolutional Neural Networks. In: 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, pp. 2560–2567 (2016)
6.
Zurück zum Zitat Kuo, C.C.J.: Understanding convolutional neural networks with a mathematical model, 3–14 (2016). arXiv: 1609(04112) Kuo, C.C.J.: Understanding convolutional neural networks with a mathematical model, 3–14 (2016). arXiv: 1609(04112)
7.
Zurück zum Zitat He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification, 6–7 (2015). arXiv: 1502(01852) He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification, 6–7 (2015). arXiv: 1502(01852)
8.
Zurück zum Zitat Cogswell, M., Ahmed, F., Girshick, R., Zitnick, L., Batra, D.: Reducing overfitting in deep networks by decorrelating representations 2(4), 4–7 (2015) Cogswell, M., Ahmed, F., Girshick, R., Zitnick, L., Batra, D.: Reducing overfitting in deep networks by decorrelating representations 2(4), 4–7 (2015)
10.
Zurück zum Zitat SuperDataScience Team: Convolutional-neural-networks-cnn-step-3-flattening, 3–4 (2018). Accessed 17 Aug 2018 SuperDataScience Team: Convolutional-neural-networks-cnn-step-3-flattening, 3–4 (2018). Accessed 17 Aug 2018
11.
Zurück zum Zitat Caruana, R., Lawrence, S., Giles, C.: Overfitting in neural nets: backpropagation, conjugate gradient, and early stopping. In: Advances in Neural Information Processing Systems, vol. 13, pp. 402–408 (2000) Caruana, R., Lawrence, S., Giles, C.: Overfitting in neural nets: backpropagation, conjugate gradient, and early stopping. In: Advances in Neural Information Processing Systems, vol. 13, pp. 402–408 (2000)
12.
Zurück zum Zitat Perez, L., Wang, J.: The effectiveness of data augmentation in image classification using deep learning. 1(1), 2–3 (2017) Perez, L., Wang, J.: The effectiveness of data augmentation in image classification using deep learning. 1(1), 2–3 (2017)
13.
Zurück zum Zitat Alex, K., Ilya, S., Geoffrey, E.: ImageNet classification with deep convolutional neural networks. 60(6): 84–90 (2017) Alex, K., Ilya, S., Geoffrey, E.: ImageNet classification with deep convolutional neural networks. 60(6): 84–90 (2017)
14.
Zurück zum Zitat Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014) Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
15.
Zurück zum Zitat Martens, J.: Deep learning via Hessian-free optimization. In: ICML 2010 - Proceedings, 27th International Conference on Machine Learning, pp. 735–742 (2010) Martens, J.: Deep learning via Hessian-free optimization. In: ICML 2010 - Proceedings, 27th International Conference on Machine Learning, pp. 735–742 (2010)
Metadaten
Titel
Cancer Detection Based on Image Classification by Using Convolution Neural Network
verfasst von
Mohammad Anas Shah
Abdala Nour
Alioune Ngom
Luis Rueda
Copyright-Jahr
2020
DOI
https://doi.org/10.1007/978-3-030-45385-5_25