Breast cancer is one of the most important malignant diseases in the world. In the United States, breast cancer ranks first among all oncological diseases in women and is the second leading cause of cancer mortality after lung cancer. Despite recent great success in the early detection and treatment of breast cancer, new approaches and algorithms are still being developed for early diagnosis. Breast cancer has many classifications, like other malignant diseases: histological, molecular, functional, TNM classification. Most cases of cancer can be diagnosed in the later stages of the disease, and treatment is often not responding and the patient is lost. Therefore, early detection of breast cancer is vital. This study uses the UCI Breast Cancer Wisconsin (Diagnostic) Data Set (WDBC), which is presented by measuring test classification accuracy, sensitivity, and specificity values. The data set was divided into 70% for the training phase and 30% for the testing phase. This study demonstrates the importance of optimization algorithm selectiona and parameters in the diagnosis of Breast Cancer using Artificial Neural Networks and investigates how they should be chosen. The accuracy results of different optimization algorithms and parameter values are reported.
Aleksandroviç, X.K., and M.A. Ryazanov. 2016. available in http://elibrary.asu.ru/xmlui/bitstream/handle/ asu/2682/vkr.pdf?sequence=1&isAllowed=y, last accessed November, 2020.
Duchi, J., E. Hazan, and Y. Singer. 2011. Adaptive subgradient methods for online learning and stochastic optimization. Machine Learning Research, 12, 2121-2159.
Fogel, D.B., E.C. Wasson, E.M. Boughton, and V.W. Porto. 1997. A step toward computerassisted mammography using evolutionary programming and neural networks., Cancer Letters, 119 (1), 93-97.
Gorunescu, M., F. Gorunescu, and K. Revett. 2007. Investigating a Breast Cancer Dataset Using a Combined Approach: Probabilistic Neural Networks and Rough Sets, Proceedings of the 3rd ACM International Conference on Intelligent Computing and Information Systems -ICICIS07, Cairo, Egypt, 246-249.
Harwich, E., and K. Laycock. 2018. Thinking on its own: AI in the NHS, available in http://www.reform. uk/publication/thinking-on-its-own-ai-in-the-nhs/, last accessed November, 2020.
Hsiao, Y.H., Y.L. Huang, W.M. Liang, S.J. Kuo, and D.R. Chen. 2009. Characterization of benign and malignant solid breast masses: harmonic versus nonharmonic 3D power Doppler imaging, Ultrasound Medicine & Biology 35(3), 353-359.
Huang, G., Y. Sun, Z. Liu, D. Sedra, and K.Q. Weinberger. 2016. Deep networks with stochastic depth. Proceedings of the European Conference on Computer Vision, Springer, 646–661.
Huo, Z., and H. Huang. 2017. Asynchronous mini-batch gradient descent with variance reduction for non-convex optimization, Thirty-First AAAI Conference on Artificial Intelligence
Ishii, M., and A. Sato. 2017 Layer-wise weight decay for deep neural networks, Pacific-Rim Symposium on Image and Video Technology. Springer, 276–289.
Jason, The Mitre Corporation, 2017. Artificial Intelligence for Health and Health Care, available in HYPERLINK “https://www.healthit.gov/sites/default/files/jsr-17-task-%20002_aiforhealthandhealthcare12122017. pdf” https://www.healthit.gov/sites/default/files/jsr-17-task- 002_aiforhealthandhealthcare12122017.pdf , last accessed November, 2020.
Mihaylov, I., M. Nisheva, and D. Vassilev. 2019. Application of Machine Learning Models for Survival Prognosis in Breast Cancer Studies
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Revett, K., F. Gorunescu, F., M. Gorunescu, E. El-Darzi, and M. Ene. 2005. A breast cancer diagnosis system: a combined approach using rough sets and probabilistic neural Networks. Computer as a tool Eurocon, Belgrade, 1124- 1127.
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Yapay Sinir Ağı Kullanarak Meme Kanseri Hastalığının Tahmini
Year 2020,
Volume: 4 Issue: 2, 255 - 272, 30.12.2020
Günümüzde meme kanseri (breast cancer) dünyadaki en önemli kötü huylu hastalıklardan biridir. ABD'de meme kanseri, kadınlarda tüm onkolojik hastalıklar arasında birinci sırada yer alır ve akciğer kanserinden sonra onkolojide ölüm nedeninin ikincisidir. Meme kanserinin erken teşhisinde ve tedavisinde son zamanlarda elde edilen büyük başarılara rağmen, ilk aşamalarda teşhisi için yeni yaklaşımlar ve algoritmalar geliştirilmeye devam etmektedir. Meme kanseri, diğer kötü huylu hastalıklar gibi birçok sınıflandırmaya sahiptir. Histolojik, moleküler, fonksiyonel, TNM sınıflandırması bunlardan bazılarıdır. Çoğu kanser vakası hastalığın geç aşamalarında ancak teşhis edilebilir ve tedavi sıklıkla cevap vermez ve hasta kaybedilir. Bu sebepten meme kanserinin erken evrelerde teşhisi hayati önem taşır. Bu çalışmada sınıflandırma testi doğruluğunu, hassasiyet ve özgüllük değerlerini ölçerek sunmakta olan Wisconsin Meme Kanseri Teşhisi (WDBC) veri seti kullanılmaktadır. Uygulamada, veri seti eğitim aşaması için %70 ve test aşaması için %30 olarak bölünmüştür. Bu çalışma yapay sinir ağı kullanarak meme kanseri tahmininde optimizasyon algoritmalarının ve parametrelerin nasıl seçilmesi gerektiğini incelemekte ve farklı seçimlerinin nasıl sonuç verdiğini göstermektedir.
Aleksandroviç, X.K., and M.A. Ryazanov. 2016. available in http://elibrary.asu.ru/xmlui/bitstream/handle/ asu/2682/vkr.pdf?sequence=1&isAllowed=y, last accessed November, 2020.
Duchi, J., E. Hazan, and Y. Singer. 2011. Adaptive subgradient methods for online learning and stochastic optimization. Machine Learning Research, 12, 2121-2159.
Fogel, D.B., E.C. Wasson, E.M. Boughton, and V.W. Porto. 1997. A step toward computerassisted mammography using evolutionary programming and neural networks., Cancer Letters, 119 (1), 93-97.
Gorunescu, M., F. Gorunescu, and K. Revett. 2007. Investigating a Breast Cancer Dataset Using a Combined Approach: Probabilistic Neural Networks and Rough Sets, Proceedings of the 3rd ACM International Conference on Intelligent Computing and Information Systems -ICICIS07, Cairo, Egypt, 246-249.
Harwich, E., and K. Laycock. 2018. Thinking on its own: AI in the NHS, available in http://www.reform. uk/publication/thinking-on-its-own-ai-in-the-nhs/, last accessed November, 2020.
Hsiao, Y.H., Y.L. Huang, W.M. Liang, S.J. Kuo, and D.R. Chen. 2009. Characterization of benign and malignant solid breast masses: harmonic versus nonharmonic 3D power Doppler imaging, Ultrasound Medicine & Biology 35(3), 353-359.
Huang, G., Y. Sun, Z. Liu, D. Sedra, and K.Q. Weinberger. 2016. Deep networks with stochastic depth. Proceedings of the European Conference on Computer Vision, Springer, 646–661.
Huo, Z., and H. Huang. 2017. Asynchronous mini-batch gradient descent with variance reduction for non-convex optimization, Thirty-First AAAI Conference on Artificial Intelligence
Ishii, M., and A. Sato. 2017 Layer-wise weight decay for deep neural networks, Pacific-Rim Symposium on Image and Video Technology. Springer, 276–289.
Jason, The Mitre Corporation, 2017. Artificial Intelligence for Health and Health Care, available in HYPERLINK “https://www.healthit.gov/sites/default/files/jsr-17-task-%20002_aiforhealthandhealthcare12122017. pdf” https://www.healthit.gov/sites/default/files/jsr-17-task- 002_aiforhealthandhealthcare12122017.pdf , last accessed November, 2020.
Mihaylov, I., M. Nisheva, and D. Vassilev. 2019. Application of Machine Learning Models for Survival Prognosis in Breast Cancer Studies
Nesterov, Y. 1983. A method for unconstrained convex minimization problem with the rate of convergence o (1/k²), Doklady AN USSR 269, 543–547.
Ramirez-Quintana, J.A., M.I. Chacon-Murguia, and J.F. Chacon-Hinojos. 2012. Artificial Neural Image Processing Applications: A Survey. Engineering Letters, 20(1), 68-81.
Revett, K., F. Gorunescu, F., M. Gorunescu, E. El-Darzi, and M. Ene. 2005. A breast cancer diagnosis system: a combined approach using rough sets and probabilistic neural Networks. Computer as a tool Eurocon, Belgrade, 1124- 1127.
Sebastian, R. 2017. An overview of gradient descent optimization algorithms, Insight Centre for Data Analytics, NUI Galway Aylien Ltd., Dublin
Shi Z., and L. He. 2010. Application of Neural Networks in Medical Image Processing, Proceedings of the Second International Symposium on Networking and Network Security (ISNNS ’10), China, 2-4.
Smith, S.L., P.-J. Kindermans, C. Ying, and Q.V. Le. 2018. Don’t decay the learning rate, increase the batch size, in International Conference on Learning Representations (ICLR)
Staelin,D.H., and C.H. Staelin. 2011. Models for Neural Spike Computation and Cognition. CreateSpace, Seattle, Washington.
Uncini A., 2003. Audio signal processing by neural Networks, Neurocomputing, (55) 3-4, 593 – 625.
Wolberg, W. H., W.N. Street, and O.L. Mangasarian. 1992. Breast cancer Wisconsin (diagnostic) data set. UCI Machine Learning Repository
Kiknadze, M., & Gürhanlı, A. (2020). Yapay Sinir Ağı Kullanarak Meme Kanseri Hastalığının Tahmini. AURUM Journal of Engineering Systems and Architecture, 4(2), 255-272.