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UW-Madison ECE 539 - Breast Cancer Diagnosis - A Discussion of Methods

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Breast Cancer DiagnosisPresentation OutlineProblem StatementDescription of DataMethods of DiagnosisDiagnosis Through LPFuture PlansBreast Cancer DiagnosisA discussion of methodsMeena VairavanPresentation OutlineProblem StatementDescription of DataMethods of DiagnosisFuture PlansProblem StatementMy goal is to compare two computationalmethods to determine which is a moreeffective means for performing breastcancer diagnosis. The first methoduses linear programming and thesecond method uses neural networks.Both methods analyze data generated by fine needle aspiration tests.Description of DataSource of Data: Wisconsin Diagnosis Breast Cancer Database (WBCD)–Dr. William Wolberg - Department of Surgery–Professor W. Nick Street - Department of Manag. Sciences–Professor O.L. Mangasarian - CS DepartmentEach case is represented by a 30-dim. feature vector computed from a digitized fine needle aspirate of a breast mass.The features describe characteristics of the cell nuclei present in the image. –Radius, texture, smoothness, concavity, and symmetryMethods of DiagnosisMethod 1: Diagnosis through linear programming via generation of a separation plane.Method 2: Diagnosis through the use of a multi-layer perceptron model using back propagation techniques.Diagnosis Through LPA linear function was constructed to generate a separation plane to classify malignant and benign tumors.–f(x) = ’x - –f(x) > 0 for malignant cases–f(x) < 0 for benign cases–minimize misclassified points by choosing  and  to minimize distance from f(x)Future PlansFind the optimal MLP configuration for this diagnosis.–Plan to modify Professor Hu’s back propagation method for these purposes.Choose appropriate characteristics to compare the LP method and MLP methodProvide an analysis of my


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UW-Madison ECE 539 - Breast Cancer Diagnosis - A Discussion of Methods

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