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Application of Artificial Neural Network in Breast cancer Classification : A comparative Study

dc.contributor.authorNwoye, E. O.
dc.contributor.authorNwaneri, S. C.
dc.contributor.authorIruhe, N. K.
dc.contributor.authorBabatunde, A. M.
dc.date.accessioned2019-09-20T15:23:13Z
dc.date.available2019-09-20T15:23:13Z
dc.date.issued2014-06-01
dc.identifier.issn2354-4368
dc.identifier.urihttps://ir.unilag.edu.ng/handle/123456789/5943
dc.description.abstract6. Background: Breast cancer is a leading cause of death especially among women globally. The classification task of breast lump as benign or malignant is due to the experience and skill of the radiologist. However, Artificial Neural Networks (ANNs) can be developed to assist radiologists in decision making Objective: The purpose of this study is to develop ANN based models for breast cancer classification Methods: The five features of retrospective breast ultrasound data obtained from Lagos University Teaching Hospital (LUTH) consisting of 83 samples were rated using Breast Imaging Reporting and Data system (BI-RADS). The data was normalized and trained in MATLAB software version (R2009a) using a feedforward multilayer ANN with 5 inputs neurons, 10 hidden neurons and one output neuron. The hidden neurons wee increased in steps of 10 for different iterations to a maximum of 100 neurons in the hidden layer. The well known Wisconsin Breast Cancer Data (WBCD) comprising of digitized data was also trained with the same algorithm and parameters Result: The results show that ANNs performance in both cases was quite high. It was also proved that there was no direct relationship between the performance of the network and the number of hidden neurons. Conclusion: ANNs are different classifiers that can be utilized in the diagnosis of breast cancer in the country.en_US
dc.language.isoenen_US
dc.publisherJournal of Baaic Medical Sciencesen_US
dc.relation.ispartofseriesVol. 2;01
dc.subjectBreast Canceren_US
dc.subjectArtificial Neural networksen_US
dc.subjectBreast Imaging Reporting and Data System (BI-RADS)en_US
dc.subjectUltrasounden_US
dc.subjectRadiologistsen_US
dc.titleApplication of Artificial Neural Network in Breast cancer Classification : A comparative Studyen_US
dc.typeArticleen_US


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