1. Classification of Breast Cancer Cells Using Artificial Neural Networks and Support Vector Machines Emmanuel Contreras Guzman

2. The Motivation  Breast Cancer is the second most deadly type of cancer in women worldwide.  1.3 million women diagnosed worldwide  Nearly half-a-million women dying from this disease each year.  Very curable if diagnosed early.  Cervical Cancer is the third most deadly type of cancer in women worldwide.  Half-a-million women diagnosed.  250,000 women dying from this disease each year.  Also very curable if diagnosed early.

3. The Data Set Breast Cancer Wisconsin (Original) Data Set - 699 Samples collected from a minimally invasive fine-needle aspirate (FNA). 458 benign (65.5%) and 241 malignant (34.5%) 9 Features (scale from 1-10): Clump thickness Uniformity of cell size Uniformity of cell shape Marginal adhesion Single epithelial cell size Bare nuclei Bland chromatin Normal nucleoli Mitoses Cervical Cancer Data 5 Features: Amount of cytoplasm Nuclei count Nuclei shape Nuclei texture Nuclei area

4. Pre-Processing Unknown Samples  16 incomplete data samples - bare nuclei  Samples used for analysis: 683 Normalization  Normalize value to between 0 - 1

5. Artificial Neural Network Analysis  MATLAB driver program  Network Configurations:  1, 2 or 3 hidden layers  Each layer with 3, 5 or 7 perceptrons  70% training  15% for testing  15% for validation Transfer functions used:  Scaled Conjugate Gradient  Logistic  Tan Sigmoid Network retrained 50 times with random sample with replacement.

6. Support Vector Machine Analysis  MATLAB driver program  Parameters Tuned:  Kernels  Radial Basis Function  Linear Kernel  Polynomial Kernel - degrees 2 and 3  Box Constraint/C - support vector cost/penalty  1e-5 to 1e5 - increasing by factor of 10  Kernel Scale/Gamma - individual examples influence the hyperplane  1e-5 to 1e5 - increasing by factor of 10 - 10-way cross validation

7. Artificial Neural Network Results Configurations with 97.4% accuracy  The Scaled Conjugate Gradient (SCG) without normalization and configuration: [5], [7], [7 7]  The Scaled Conjugate Gradient (SCG) with normalization and configuration: [5 5], [7]  The Logistic transfer function (logsig) without normalization and configuration: [5 5], [7]  The Logistic transfer function (logsig) with normalization and configuration: [5], [7]  The Tan-Sigmoid transfer function (tansig) without normalization:[5], [5 5], [7], [7 7]  The Tan-Sigmoid transfer function (tansig) with normalization:[7], [7 7], [7 7 7] Sensitivity: 98% Specificity: 96%

8. Support Vector Machine Results Maximum accuracy: 94.24%  2nd-order polynomial kernel  Box constraint of 1e-1  kernel scale (gamma) of 1  Sensitivity: 96.58%  Specificity: 91.43% Second most accurate configurations, accuracy: 94.13%  Radial Basis Function kernel, box constraint of 1, kernel scale set to auto,  Polynomial degree 3 with box constraint of 1e-1 and kernel scale set to auto.  Sensitivity: 96.35%  Specificity 91.02%

9. Conclusion  None of the ANN layer configurations with 3 perceptrons achieved a maximum accuracy of 97.4%.  ANN configurations with 3+ layers and perceptrons do not generalize well, and overfit the data.  Classification of breast cancer data using an ANN should be kept to one or two hidden layers of about 5 perceptrons in order to achieve the highest classification accuracy.  In comparison to the SVM, the neural network achieved higher accuracy by 3%, sensitivity by 2% and specificity by 5%.  A neural network appears to be a better algorithm for classifying the data.

10. Discussion  Other configurations for the ANN and SVM which were not analyzed.  More fine tuning of parameters.  Artificial Neural Network  Learning rates, transfer functions, more/less layers/perceptrons  Support Vector Machines  More Kernels  Different cost function  Removing “bare nuclei” feature and using all 699 samples.