Breast cancer diagnosis using artificial intelligence neural networks

المؤلفون المشاركون

Abd al-Qadir, Howida Ali
Hamzah, Muhammad Hasan

المصدر

Journal of Science and Technology

العدد

المجلد 12، العدد 1 (30 يونيو/حزيران 2011)، ص ص. 159-171، 13ص.

الناشر

جامعة السودان للعلوم و التكنولوجيا عمادة البحث العلمي

تاريخ النشر

2011-06-30

دولة النشر

السودان

عدد الصفحات

13

التخصصات الرئيسية

الطب البشري
تكنولوجيا المعلومات وعلم الحاسوب

الموضوعات

الملخص EN

Breast cancer is the second largest cause of cancer deaths among women.

Breast cancer is one of the major tumor related cause of death in women.

Various artificial intelligence techniques have been used to improve the diagnoses procedures.

The present study aimed at detecting breast cancer by using three neural network topologies which are Multi Layer Perceptron (MLP), Generalized Regression (GRNN) and Probabilistic (PNN).

Three Wisconsin breast cancer data sets (WBCD) were examined by these networks.

It was seen that the most suitable neural network model for classifying WBCD are MLP and GRNN, and observing that PNN performance was low in case of small datasets.

KEYWORDS: Artificial neural networks, breast cancer, MLP, GRNN, PNN

نمط استشهاد جمعية علماء النفس الأمريكية (APA)

Abd al-Qadir, Howida Ali& Hamzah, Muhammad Hasan. 2011. Breast cancer diagnosis using artificial intelligence neural networks. Journal of Science and Technology،Vol. 12, no. 1, pp.159-171.
https://search.emarefa.net/detail/BIM-298736

نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)

Abd al-Qadir, Howida Ali& Hamzah, Muhammad Hasan. Breast cancer diagnosis using artificial intelligence neural networks. Journal of Science and Technology Vol. 12, no. 1 (2011), pp.159-171.
https://search.emarefa.net/detail/BIM-298736

نمط استشهاد الجمعية الطبية الأمريكية (AMA)

Abd al-Qadir, Howida Ali& Hamzah, Muhammad Hasan. Breast cancer diagnosis using artificial intelligence neural networks. Journal of Science and Technology. 2011. Vol. 12, no. 1, pp.159-171.
https://search.emarefa.net/detail/BIM-298736

نوع البيانات

مقالات

لغة النص

الإنجليزية

الملاحظات

Includes bibliographical references : p. 170-171

رقم السجل

BIM-298736