Diagnosis of Breast Cancer by Modular Evolutionary Neural Networks

1. Diagnosis of Breast Cancer by Modular Evolutionary Neural Networks Rahul Kala, School of Cybernetics, School of Systems Engineering University of Reading http://rkala.99k.org/ rkala001@gmail.com, r.kala@pgr.reading.ac.uk Publication of paper: R. Kala, R. R. Janghel, R. Tiwari, A. Shukla (2011) Diagnosis of Breast Cancer by Modular Evolutionary Neural Networks, International Journal of Biomedical Engineering and Technology, 7(2): 194 – 211. 

2. Presentation to the paper R. Kala et al. (2011) Diagnosis of Breast Cancer by Modular Evolutionary Neural Networks, International Journal of Biomedical Engineering and Technology [Accepted, In Press] Research Sponsored by: Indian Institute of Information Technology and Management Gwalior, INDIA

3. Biomedical Engineering

4. The Problem

5. Data Set Data Set Available At: W. H. Wolberg, O. L. Mangasarian, D. W. Aha. UCI Machine Learning Repository [http://www.ics.uci.edu/~mlearn/MLRepository.html], University of Wisconsin Hospitals, 1992.

6. Machine Learning Perspective

7. Not in agenda

8. Generate random individuals No While Stopping Criterion not met Return best individual Yes Selection Create Neural Network as per individual specifications Crossover Mutation Training Algorithm as local search strategy Elite Performance/Connection Penalty Evaluation Fitness Evaluation Concept – 1: Evolutionary Neural Network System 1 System 2

9. Attribute Set 1 Evolutionary Neural Net 1 Result Integration Attribute Division Inputs Output Attribute Set 2 Evolutionary Neural Net 2 Concept – 2: Attribute Division

10. Cluster 1 Attribute Division 1 Input Space Clustering Cluster 2 Attribute Division 2 Result Integration Inputs Output Cluster 3 Attribute Division 3 Concept – 3: Input Space Division

11. Inputs Input Space Clustering Cluster 1 Cluster 2 Cluster 3 Attribute Set 1 Attribute Set 2 Attribute Set 1 Attribute Set 2 Attribute Set 1 Attribute Set 2 Evolutionary Neural Net 1 Evolutionary Neural Net 2 Evolutionary Neural Net 2 Evolutionary Neural Net 2 Evolutionary Neural Net 2 Evolutionary Neural Net 2 Result Integration Result Integration Result Integration Cluster Result Integration Output Concept – 3: Input Space Division

12. Inputs Same input to all Experts Expert 1: Multi Layer Perceptron-1 Expert 2: Radial Basis Function Network Expert 3: Multi Layer Perceptron-2 Result Integration Output Concept 4: Mixture of Experts

13. Results

14. Component Results

15. Related Publications - Journals • Kala, Rahul, Tiwari, Ritu, & Shukla, Anupam (2011) Breast Cancer Diagnosis using Optimized Attribute Division in Modular Neural Networks, Journal of Information Technology Research, Vol. 4, No 1, pp 34-47 • Kala, Rahul, Janghel, Rekh Ram, Tiwari, Ritu, & Shukla, Anupam, (2011) Diagnosis of Breast Cancer by Modular Evolutionary Neural Networks, International Journal of Biomedical Engineering and Technology, Inderscience[In Press] • Kala, Rahul, Vazirani, Harsh, Khawalkar, Nishant, & Bhattacharya, Mahua (2010) Evolutionary Radial Basis Function Network for Classificatory Problems, International Journal of Computer Science Applications, TMRF India, Vol. 7, No. 4, pp 34-49 • Kala, Rahul, Vazirani, Harsh, Shukla, Anupam, & Tiwari, Ritu (2010) Medical Diagnosis using Incremental Evolution of Neural Network, Journal of Hybrid Computing Research, TMRF India, Vol. 3, No. 1, pp 9-17 • Kala, Rahul, Vazirani, Harsh, Shukla, Anupam, & Tiwari, Ritu (2010) Evolution of Modular Neural Network in Medical Diagnosis, International Journal of Applied Artificial Intelligence in Engineering System, Vol. 2, No. 1, pp 49 -58

16. Related Publications - Conferences • Meena, Yogesh Kumar, Arya, Karam Veer, Kala, Rahul (2010) Classification using Redundant Mapping in Modular Neural Networks, Proceedings of the 2010 World Congress on Nature and Biologically Inspired Computing, Kitakyushu, Japan [In Press] • Janghel, R. R., Shukla, Anupam, Tiwari, Ritu, Kala, Rahul (2010) Breast Cancer Diagnostic System using Symbiotic Adaptive Neuro-evolution (SANE). Proceedings of the 2010 International Conference of Soft Computing and Pattern Recognition, Cercy Pontoise/Paris, France, pp 326-329. • Janghel, R. R., Shukla, Anupam, Tiwari, Ritu, Kala, Rahul (2010) Breast Cancer Diagnosis using Artificial Neural Network Models. Proceedings of the IEEE 3rd International Conference on Information Sciences and Interaction Sciences, pp 89-94, Chengdu, China. • Vazirani, Harsh, Kala, Rahul, Shukla, Anupam, Tiwari, Ritu (2010) Diagnosis of Breast Cancer by Modular Neural Network. Proceedings of the Third IEEE International Conference on Computer Science and Information Technology, pp 115-119, Chengdu, China • Kala, Rahul, Shukla, Anupam, & Tiwari, Ritu (2009) Comparative analysis of intelligent hybrid systems for detection of PIMA indian diabetes, Proceedings of the IEEE 2009 World Congress on Nature & Biologically Inspired Computing, NABIC '09, pp 947 - 952, Coimbatote, India • Kala, Rahul, Shukla, Anupam, Tiwari, Ritu (2009) Fuzzy Neuro Systems for Machine Learning for Large Data Sets, Proceedings of the IEEE International Advance Computing Conference, IACC '09, pp 541-545, Patiala, India

17. More from the authors

18. Thank You Rahul Kala Call Centre Lab, Room No 188, School of Cybernetics, School of Systems Engineering, University of Reading, Whiteknights http://rkala.99k.org/ rkala001@gmail.com r.kala@pgr.reading.ac.uk Ph: +44 (0) 7424752843 Acknowledgements Prof. AnupamShukla, Professor, IIITM Gwalior Dr. RituTiwari, Assistant Professor, IIITM Gwalior Mr. R. R. Janghel, Research Scholar, IIITM Gwalior