과제정보
The author thanks to Scientific Research Deanship, Albaha University for the research funding under grant no.: 026//1439.
참고문헌
- Siegel R.L, Miller K.D, Jemal A, Cancer statistics, 2017, DOI: https://doi.org/10.3322/caac.21387.
- Elmore J.G, Nakano C.Y, Koepsell T.D, Desnick L.M, Ransoho D.F, International variation in screening mammography interpretations in community-based programs. J Natl Cancer Inst, 2003;95(18):1384-1393. https://doi.org/10.1093/jnci/djg048
- Veronesi U, Boyle P, Goldhirsch A, Orecchia R, Viale G, Breast cancer. Lancet, 2005; 365:17271741.
- Raimond W. M, Giard MD, Jo Hermans: The value of aspiration cytologic examination of the breast a statistical review of the medical literature. American Cancer Society, 1992;69(8):2104-2110. https://doi.org/10.1002/1097-0142(19920415)69:8<2104::AID-CNCR2820690816>3.0.CO;2-O
- Elmore, J.G; Armstrong K, Lehman C.D, Fletcher S.W, Screening for breast cancer. The Journal of the American Medical Association, 2005;293(10):1245-56. https://doi.org/10.1001/jama.293.10.1245
- Gayathri. B.M, Sumathi C.P, Santhanam T, Breast cancer diagnosis using machine learning algorithms -a survey, International Journal of Distributed and Parallel Systems (IJDPS), 2013; 4(3). DOI : 10.5121/ijdps.2013.4309 105.
- Borges L.R. Analysis of the Wisconsin Breast Cancer Dataset and machine learning for breast cancer detection, XI Workshop de Visao Computacional, WVC'2015, Sao Carlos - SP - Brazil, 5-7 October, 2015:15-19.
- Karbab E.B, Debbabi M, Derhab A, Mouheb D. MalDozer: Automatic framework for android malware detection using deep learning, Proceedings of the Fifth Annual DFRWS Europe, Digital Investigation, 2018;24:S48-S59. DOI: https://doi.org/10.1016/j.diin.2018.01.
- Litjens G, Kooi T, Bejnordi B.E, Setio A.A.A, Ciompi F, Ghafoorian M, van der Laak J.A.W.M, van Ginneken B, S'anchez C.I, A Survey on deep learning in medical image analysis, Medical Image Analysis Journal, 2017;42:60-88, DOI: https://doi.org/10.1016/j.media.2017.07.005
- Fukushima K. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, 1980;36 (4), 193-202. https://doi.org/10.1007/BF00344251
- Lo S.-C, Lou S.-L, Lin J.-S, Freedman, M.T, Chien, M.V, Mun, S. K. Artificial convolution neural network techniques and applications for lung nodule detection. IEEE Transactions on Medical Imaging, 1995;14:711-718. https://doi.org/10.1109/42.476112
- Lecun Y, Bottou L, Bengio Y, Ha ner P. Gradient-based learning applied to document recognition. Proceedings of the IEEE 86, 1998;2278-2324. https://doi.org/10.1109/5.726791
- Krizhevsky A, Sutskever I., Hinton G. Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems. 2012;1097-1105.
- Russakovsky, O, Deng, J, Su, H, Krause, J., Satheesh, S., Ma, S, Huang, Z., Karpathy, A, Khosla, A, Bernstein, M., Berg, A C, Fei-Fei, L. ImageNet large scale visual recognition challenge. International Journal of Computer Vision, 2014;115 (3), 1-42. https://doi.org/10.1007/s11263-015-0816-y
- Goodfellow I, Bengio Y and Courville A. Deep Learning, MIT Press, 2016.
- Lecun Y, Bengio, Y Hinton G. Deep learning. Nature, 2015;521 (7553), 436-444. https://doi.org/10.1038/nature14539
- O'Shea K & Nash R. An Introduction to Convolutional Neural Networks. ArXiv e-prints, 2015.
- Kim Y. Convolutional neural networks for sentence classification, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, 25-29 October, 2014:1746-1751.
- Pennington J, Socher R. et al., GloVe: Global Vectors for Word Representation, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, 25-29 October, 2014: 1532-1543.
- University of Wisconsin-Madison. Machine Learning for Cancer Diagnosis and Prognosis. http://pages.cs.wisc.edu/olvi/uwmp/cancer.html.
- Wolberg W.H, Street W.N, Heisey D.M, and Mangasarian O.L. Computer-derived nuclear features distinguish malignant from benign breast cytology, Human Pathology, 1995;26:792--796. https://doi.org/10.1016/0046-8177(95)90229-5
- Wolberg W.H, Street W.N, and Mangasarian O.L. Image analysis and machine learning applied to breast cancer diagnosis and prognosis. Analytical and Quantitative Cytology and Histology, 1995;17(2);77-87, April 1995.
- Mu T. Breast cancer diagnosis from fine-needle aspiration using supervised compact hyperspheres and establishment of confidence of malignancy Tingting Mu, Asoke K. Nandi A.K. 16th European Signal Processing Conference (EUSIPCO 2008), Lausanne, Switzerland, August 25-29, 2008.
- Tensorflow - https://www.tensorflow.org (2017).
- Bennett K. P. and Mangasarian, O.L. Neural Network Training via Linear Programming. Advances in Optimization and Parallel Computing, Pardalos P.M.(Ed.),Elsevier Science Publishers B. V., 1992:56-67.
- Pena-Reyes C.A. and Sipper M. A Fuzzy-genetic approach to breast cancer diagnosis. Artificial Intelligence in Medicine, Elsevier, 1998;17(2):131-55. https://doi.org/10.1016/S0933-3657(99)00019-6
- Kiyan T. and Yildirim Y. Breast cancer diagnosis using statistical neural networks. Journal of Electrical & Eletronics Engineering, Istanbul University, 2004;4(2):1149-1153.
- Sahan S, Polat K, Kodaz H. Gunes S. A new hybrid method based on fuzzy-artificial immune system and k-NN algorithm for breast cancer diagnosis, Computers in Biology and Medicine, 2001;37:415-423. https://doi.org/10.1016/j.compbiomed.2006.05.003
- Akay M.F. Support vector machines combined with feature selection for breast cancer diagnosis. Expert Systems with Applications, 2008;36:3240-3247. https://doi.org/10.1016/j.eswa.2008.01.009
- Paulin F, Santhakumaran A. Classification of Breast cancer by comparing Back propagation training algorithms, International Journal on Computer Science and Engineering (IJCSE), 2011;3: 327-332.