BREAST CANCER DIAGNOSIS BY ARTIFICIAL NEURAL NETWORKS (ANN’S)

Hassan, Ayat Mahmoud; Ali, Roaa Mohammed; Bilal, Shaza Babiker; Supervised, -Eltahir Mohammed Hussein

dc.contributor.author	Hassan, Ayat Mahmoud
dc.contributor.author	Ali, Roaa Mohammed
dc.contributor.author	Bilal, Shaza Babiker
dc.contributor.author	Supervised, -Eltahir Mohammed Hussein
dc.date.accessioned	2016-02-15T07:53:33Z
dc.date.available	2016-02-15T07:53:33Z
dc.date.issued	2015-10-01
dc.identifier.citation	Hassan,Ayat Mahmoud.BREAST CANCER DIAGNOSIS BY ARTIFICIAL NEURAL NETWORKS (ANN’S)/Ayat Mahmoud Hassan,Roaa Mohammed Ali,Shaza Babiker Bilal;Eltahir Mohammed Hussein.-khartoum :Sudan University of Sciences and Technology,College of Engineering,2015.-45p:ill;28cm.-Bachelors search	en_US
dc.identifier.uri	http://repository.sustech.edu/handle/123456789/12737
dc.description	Bachelors search	en_US
dc.description.abstract	Breast cancer is the second largest cause of cancer deaths among women. This project aim for early diagnosis of breast cancer and reduce the human diagnosis errorsthrough (ANN’s). Here we canstudy the performance of different Neural Network structures: Radial Basis Function(RBF), General Regression Neural Network (GRNN)and Back propagation NeuralNetwork(BPNN), are examined on the Wisconsin Breast Cancer Data (WBCD).The result demonstrated that; the BPNN is the best classification accuracy.	en_US
dc.description.sponsorship	Sudan University of Sciences and Technology	en_US
dc.language.iso	en	en_US
dc.publisher	Sudan University of Sciences and Technology	en_US
dc.subject	BREAST CANCER	en_US
dc.subject	ARTIFICIAL NEURAL NETWORKS	en_US
dc.subject	NEURAL NETWORKS	en_US
dc.subject	DIAGNOSIS	en_US
dc.title	BREAST CANCER DIAGNOSIS BY ARTIFICIAL NEURAL NETWORKS (ANN’S)	en_US
dc.type	Thesis	en_US