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The Scientific World Journal
Volume 2014, Article ID 351518, 11 pages
http://dx.doi.org/10.1155/2014/351518
Research Article

DERMA: A Melanoma Diagnosis Platform Based on Collaborative Multilabel Analog Reasoning

1La Salle, Ramon Llull University, Quatre Camins 2, 08022 Barcelona, Spain
2Melanoma Unit, Dermatology Department, Clinic Hospital, Rosselló 149, 08036 Barcelona, Spain

Received 7 August 2013; Accepted 12 November 2013; Published 21 January 2014

Academic Editors: S. Jagannathan and S. Sessa

Copyright © 2014 Ruben Nicolas et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. E. McWhirter and L. Hoffman-Goetz, “Visual images for patient skin self-examination and melanoma detection: a systematic review of published studies,” Journal of the American Academy of Dermatology, vol. 69, no. 1, pp. 47–55, 2013. View at Publisher · View at Google Scholar
  2. A. Aamodt and E. Plaza, “Case-based reasoning: foundational issues, methodological variations, and system approaches,” AI Communications, vol. 7, no. 1, pp. 39–59, 1994. View at Google Scholar · View at Scopus
  3. S. Segura, S. Puig, C. Carrera, J. Palou, and J. Malvehy, “Dendritic cells in pigmented basal cell carcinoma: a relevant finding by reflectance-mode confocal microscopy,” Archives of Dermatology, vol. 143, no. 7, pp. 883–886, 2007. View at Publisher · View at Google Scholar · View at Scopus
  4. A. Scope, C. Benvenuto-Andrade, A.-L. C. Agero et al., “In vivo reflectance confocal microscopy imaging of melanocytic skin lesions: consensus terminology glossary and illustrative images,” Journal of the American Academy of Dermatology, vol. 57, no. 4, pp. 644–658, 2007. View at Publisher · View at Google Scholar · View at Scopus
  5. J. Avila, E. Gibaja, and S. Ventura, “Multi-label classification with gene expression programming,” in Hybrid Artificial Intelligence Systems, vol. 5572 of Lecture Notes in Computer Science, pp. 629–637, Springer, Berlin, Germany, 2009. View at Publisher · View at Google Scholar
  6. P. E. Xing, Y. A. Ng, M. I. Jordan, and S. Russell, “Distance metric learning, with application to clustering with side-information,” in Advances in Neural Information Processing Systems 15, pp. 505–512, The MIT Press, London, UK, 2002. View at Google Scholar
  7. A. Jain, A. Jain, and S. Jain, Artificial Intelligence Techniques in Breast Cancer Diagnosis and Prognosis, Series in Machine Perception and Artificial Intelligence, World Scientific, Hackensack, NJ, USA, 2000.
  8. N. Singh, A. G. Mohapatra, and G. Kanungo, “Breast cancer mass detection in mammograms using K-means and fuzzy C-means clustering,” International Journal of Computer Applications, vol. 22, no. 2, pp. 15–21, 2011. View at Publisher · View at Google Scholar
  9. C.-R. Nicandro, M.-M. Efrén, A.-A. María Yaneli et al., “Evaluation of the diagnostic power of thermography in breast cancer using bayesian network classifiers,” Computational and Mathematical Methods in Medicine, vol. 2013, Article ID 264246, 10 pages, 2013. View at Publisher · View at Google Scholar
  10. A. Fornells, J. M. Martorell, E. Golobardes, J. M. Garrell, and X. VilasÍs, “Patterns out of cases using kohonen maps in breast cancer diagnosis,” International Journal of Neural Systems, vol. 18, no. 1, pp. 33–43, 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. E. Armengol and S. Puig, “Combining two lazy learning methods for classification and knowledge discovery,” in Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (INSTICC '11), pp. 200–207, Paris, France, 2011.
  12. A. Fornells, E. Golobardes, E. Bernadó, and J. Martí-Bonmatí, “Decision support system for breast cancer diagnosis by a meta-learning approach based on grammar evolution,” in Proceedings of the 8th International Conference on Enterprise Information Systems (ICEIS '06), pp. 222–229, May 2006. View at Scopus
  13. A. Fornells, E. Armengol, E. Golobardes, S. Puig, and J. Malvehy, “Experiences using clustering and generalizations for knowledge discovery in melanomas domain,” in Advances in Data Mining. Medical Applications, E-Commerce, Marketing, and Theoretical Aspects, vol. 5077 of Lecture Notes in Computer Science, pp. 57–71, Springer, Berlin, Germany, 2008. View at Publisher · View at Google Scholar
  14. E. Armengol, “Classification of melanomas in situ using knowledge discovery with explained case-based reasoning,” Artificial Intelligence in Medicine, vol. 51, no. 2, pp. 93–105, 2011. View at Publisher · View at Google Scholar · View at Scopus
  15. V. Ahlgrimm-Siess, M. Laimer, E. Arzberger, and R. Hofmann-Wellenhof, “New diagnostics for melanoma detection: from artificial intelligence to RNA microarrays,” Future Oncology, vol. 8, no. 7, pp. 819–827, 2012. View at Google Scholar
  16. S. M. Rajpara, A. P. Botello, J. Townend, and A. D. Ormerod, “Systematic review of dermoscopy and digital dermoscopy/artificial intelligence for the diagnosis of melanoma,” British Journal of Dermatology, vol. 161, no. 3, pp. 591–604, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. Y. Kalfoglou and M. Schorlemmer, “Ontology mapping: the state of the art,” The Knowledge Engineering Review, vol. 18, no. 1, pp. 1–31, 2003. View at Publisher · View at Google Scholar · View at Scopus
  18. T. G. Dietterich, “Ensemble learning,” in The Handbook of Brain Theory and Neural Networks, pp. 405–408, The MIT Press, Cambridge, Mass, USA, 2002. View at Google Scholar
  19. R. Nicolas, E. Golobardes, A. Fornells, S. Puig, C. Carrera, and J. Malvehy, “Identification of relevant knowledge for characterizing the melanoma domain,” in 2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics (IWPACBB ’08), vol. 49 of Advances in Soft Computing, pp. 55–59, Springer, Berlin, Germany, 2009. View at Publisher · View at Google Scholar
  20. J. Hartigan and M. Wong, “A k-means clustering algorithm,” in Applied Statistics, vol. 28, pp. 100–108, 1979. View at Google Scholar
  21. T. Kohonen, Self-Organizing Maps, Springer Series in Information Sciences, Springer, New York, NY, USA, 3rd edition, 2000.
  22. D. Vernet, R. Nicolas, E. Golobardes et al., Pattern Discovery in Melanoma Domain Using Partitional Clustering, vol. 184 of Frontiers in Artificial Intelligence and Applications, IOS Press, Amsterdam, The Netherlands, 2008. View at Publisher · View at Google Scholar
  23. A. Fornells, E. Golobardes, J. M. Martorell, J. M. Garrell, E. Bernado, and N. Macia, “A methodology for analyzing the case retrieval from a clustered case memory,” in Case-Based Reasoning Research and Development, vol. 4626 of Lecture Notes in Computer Science, pp. 122–136, Springer, Berlin, Germany, 2007. View at Publisher · View at Google Scholar
  24. S. Wess, K. D. Althoff, and G. Derwand, “Using k-d trees to improve the retrieval step in case-based reasoning,” in Topics in Case-Based Reasoning, vol. 837 of Lecture Notes in Artificial Intelligence, pp. 167–181, Springer, Berlin, Germany, 1994. View at Publisher · View at Google Scholar
  25. M. Lenz, H. D. Burkhard, and S. Brückner, “Applying case retrieval nets to diagnostic tasks in technical domains,” in Advances in Case-Based Reasoning, vol. 1168 of Lecture Notes in Artificial Intelligence, pp. 219–233, Springer, Berlin, Germany, 1996. View at Publisher · View at Google Scholar
  26. Q. Yang and J. Wu, “Enhancing the effectiveness of interactive case-based reasoning with clustering and decision forests,” Applied Intelligence, vol. 14, no. 1, pp. 49–64, 2001. View at Publisher · View at Google Scholar · View at Scopus
  27. E. L. Rissland and J. J. Daniels, “The synergistic application of CBR to IR,” Artificial Intelligence Review, vol. 10, no. 5-6, pp. 441–475, 1996. View at Publisher · View at Google Scholar · View at Scopus
  28. A. Fornells, E. Golobardes, D. Vernet, and G. Corral, “Unsupervised case memory organization: analysing computational time and soft computing capabilities,” in Advances in Case-Based Reasoning, vol. 4106 of Lecture Notes in Computer Science, pp. 241–255, Springer, Berlin, Germany, 2006. View at Publisher · View at Google Scholar
  29. A. Fornells, J. Camps, E. Golobardes, and J. M. Garrell, “Comparison of strategies based on evolutionary computation for the design of similarity functions,” in Artificial Intelligence Research and Development, vol. 131, pp. 231–238, IOS Press, Amsterdam, The Netherlands, 2005. View at Google Scholar
  30. Y. Liu, Y. Yang, and J. Carbonell, “Boosting to correct inductive bias in text classification,” in Proceedings of the 11st International Conference on Information and Knowledge Management (CIKM '02), pp. 348–355, ACM Press, New York, NY, USA, November 2002. View at Publisher · View at Google Scholar · View at Scopus
  31. R. Nicolas, D. Vernet, E. Golobardes, A. Fornells, F. De la Torre, and S. Puig, “Distance metric learning in a collaborative melanoma diagnosis system with case-based reasoning,” in Proceedings of the 14th United Kingdom Workshop on Case-Based Reasoning at the 29th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, pp. 58–66, CMS Press, University of Greenwich, London, UK, 2009. View at Google Scholar
  32. E. Bauer and R. Kohavi, “An empirical comparison of voting classification algorithms: bagging, boosting, and variants,” Machine Learning, vol. 36, no. 1-2, pp. 105–139, 1999. View at Publisher · View at Google Scholar · View at Scopus
  33. K. T. Leung and D. S. Parker, “Empirical comparisons of various voting methods in bagging,” in Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '03), pp. 595–600, ACM Press, New York, NY, USA, 2003. View at Publisher · View at Google Scholar
  34. R. Avogadri and G. Valentini, “Fuzzy ensemble clustering for DNA microarray data analysis,” in Applications of Fuzzy Sets Theory, vol. 4578 of Lecture Notes in Computer Science, pp. 537–543, Springer, Berlin, Germany, 2007. View at Publisher · View at Google Scholar
  35. R. E. Abdel-Aal, “Abductive network committees for improved classification of medical data,” Methods of Information in Medicine, vol. 43, no. 2, pp. 192–201, 2004. View at Google Scholar · View at Scopus
  36. S. Ontañón, Ensemble Case-Based Learning for Multi-Agent Systems, VDM, Saarbrücken, Germany, 2008.
  37. P. Melville and R. J. Mooney, “Diverse ensembles for active learning,” in Proceedings of the 21st International Conference on Machine Learning (ICML '04), pp. 584–591, Banff, Canada, July 2004. View at Publisher · View at Google Scholar · View at Scopus
  38. R. Nicolas, E. Golobardes, A. Fornells et al., Using Ensemblebased Reasoning to Help Experts in Melanoma Diagnosis, vol. 184 of Frontiers in Artificial Intelligence and Applications, IOS Press, Amsterdam, The Netherlands, 2008.
  39. R. Nicolas, D. Vernet, E. Golobardes, A. Fornells, S. Puig, and J. Malvehy, “Improving the combination of CBR systems with preprocessing rules in melanoma domain,” in Proceedings of the 8th International Conference on Case-Based Reasoning, pp. 225–234, Seattle, Wash, USA, 2009.
  40. T. K. Ho, M. Basu, and M. Law, “Measures of complexity in classification problems,” in Data Complexity in Pattern Recognition, Advanced Information and Knowledge Processing, pp. 1–23, Springer, London, UK, 2006. View at Publisher · View at Google Scholar
  41. G. Tsoumakas and I. Vlahavas, “Random K-labelsets: an ensemble method for multilabel classification,” in Machine Learning: ECML 2007, vol. 4701 of Lecture Notes in Computer Science, pp. 406–417, 2007. View at Publisher · View at Google Scholar · View at Scopus
  42. M.-L. Zhang and Z.-H. Zhou, “ML-KNN: a lazy learning approach to multi-label learning,” Pattern Recognition, vol. 40, no. 7, pp. 2038–2048, 2007. View at Publisher · View at Google Scholar · View at Scopus
  43. J. Han and M. Kamber, Data Mining: Concepts and Techniques, The Morgan Kaufmann Series in Data Management Systems, Morgan Kaufmann, San Francisco, Calif, USA, 2006.
  44. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, “The WEKA data mining software: an update,” SIGKDD Explorations, vol. 11, no. 1, pp. 10–18, 2009. View at Publisher · View at Google Scholar
  45. J. Read and A. Pruned, “Problem transformation method for multi-label classification,” in Proceedings of the 6th New Zealand Computer Science Research Student Conference (NZCSRSC' 08), pp. 143–150, Christchurch, New Zealand, 2008.
  46. M.-L. Zhang and Z.-H. Zhou, “Multilabel neural networks with applications to functional genomics and text categorization,” IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 10, pp. 1338–1351, 2006. View at Publisher · View at Google Scholar · View at Scopus
  47. R. E. Schapire and Y. Singer, “Boostexter: a boosting-based system for text categorization,” Machine Learning, vol. 39, no. 2, pp. 135–168, 2000. View at Publisher · View at Google Scholar · View at Scopus
  48. J. Ross Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann Series in Machine Learning, Morgan Kaufmann, San Francisco, Calif, USA, 1993.
  49. A. Clare and R. D. King, “Knowledge discovery in multi-label phenotype data,” in Principles of Data Mining and Knowledge Discovery, vol. 2168 of Lecture Notes in Computer Science, pp. 42–53, Springer, Berlin, Germany, 2001. View at Publisher · View at Google Scholar
  50. A. Elisseeff, J. Weston, and A. Kernel, “Method for multi-labelled classification,” in Advances in Neural Information Processing Systems, vol. 14, pp. 681–687, The MIT Press, Cambridge, Mass, USA, 2001. View at Google Scholar
  51. R. Nicolas, A. Sancho-Asensio, E. Golobardes, A. Fornells, and A. O. Puig, “Multi-label classification based on analog reasoning,” Expert Systems with Applications, vol. 40, no. 15, pp. 5924–5931, 2013. View at Publisher · View at Google Scholar
  52. A. Frank and A. Asuncion, UCI Machine Learning Repository, University of California, Berkeley, Calif, USA, 2010.