Multilayer Perceptron Approach in Breast Cancer Diagnosis

Subject Areas : Majlesi Journal of Telecommunication Devices

Emine Avşar Aydin ¹ , Gözde Saribaş ²

1 - 1- Adana Alparslan Türkeş Science and Technology University, Department of Aerospace Engineering, Adana, Turkey.
2 - Çukurova University, Department of Biomedical Engineering, Adana, Turkey.

Received: 2020-11-22 Accepted : 2020-11-25 Published : 2020-12-01

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References:

[1] F. E. Aslan and A. Gürkan, “Kadinlarda meme kanseri risk düzeyi,” Meme Sağliği Derg., Vol. 3, pp. 63–68, 2007.

[2] Acıbadem, “Kanser Nedir?,” T.C. Sağlık Bakanlığı Halk Sağlığı Genel Müdürlüğü Kanser Dairesi Başkanlığı, 1991., Retrieved from https://www.acibadem.com.tr/acibadem-de/kanser-onkoloji/

[3] Dua, D. and Graff, C. (2019). UCI Machine Learning Repositor, Retrieved from [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

[4] O. TAŞOVA, “Yapay sinir ağları ile yüz tanıma.” DEÜ Fen Bilimleri Enstitüsü, 2011.

[5] G. E. Hinton, S. Osindero, and Y.-W. Teh, "A fast learning algorithm for deep belief nets," Neural Comput., Vol. 18, No. 7, pp. 1527–1554, 2006.

[6] A. G. Parlos, B. Fernandez, A. F. Atiya, J. Muthusami, and W. K. Tsai, "An accelerated learning algorithm for multilayer perceptron networks," IEEE Trans. Neural Networks, Vol. 5, No. 3, pp. 493–497, 1994.

[7] Stephen, "Perceptron-based learning algorithms," IEEE Trans. neural networks, Vol. 50, No. 2, p. 179, 1990.

[8] Veri Madenciliğinde Sınıflandırma Algoritmalarının Bir Örnek Üzerinde Karşılaştırılması Cengiz COŞKUN1, Yrd. Doç. Dr. Abdullah BAYKAL2