| Peer-Reviewed

Categorization of Non-Melanoma Skin Lesion Diseases Using Support Vector Machine and Its Variants

Received: 25 February 2015     Accepted: 12 March 2015     Published: 18 March 2015
Views:       Downloads:
Abstract

Skin cancer is the growth of uncontrolled abnormal skin cells. There are two main types of skin cancers such as Melanoma and Non-Melanoma. The main objective of this research work is to focus on Non-Melanoma skin cancers and classify the types of it.The classification of non melanoma skin cancers is automated using machine learning approach and the model is built to predict the type of disease accurately using support vector machine and its variants. Various experiments have been carried out with skin lesion images and the results are analyzed.

Published in International Journal of Medical Imaging (Volume 3, Issue 2)
DOI 10.11648/j.ijmi.20150302.15
Page(s) 34-40
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Classification, Machine Learning, Prediction, Support Vector Machine, Training

References
[1] G. R. Day & R. H. Barbour. “Automated melanoma diagnosis: where are we at?” Skin Research and Technology 6, pp. 1–5,2000.
[2] Lucia Ballerina, Robert B. Fisher Ben Aldridgey, Jonathan Reesy “Non-melanoma skin lesion classification using colour image data in a Hierarchical k-nn classifier”
[3] M. E. Celebi, W. V. Stoecker, and R. H. Moss, “Advances in skin cancer image analysis,” Computerized Medical Imaging and Graphics, vol. 35, No. 2, Pp. 83 – 84, 2011.
[4] M. EmreCelebi and Hassan and A. Kingravi“A methodological approach to the classification of dermoscopyimages”Comput Med Imaging Graph. 2007 September; 31(6): 362–373.
[5] José FernándezAlcón, CalinaCiuhu, Warner ten Kate, Adrienne Heinrich, NatalliaUzunbajakava, GertruudKrekels, Denny Siem, and Gerard de Haan-2009”Automatic Imaging System With Decision Support for Inspection of Pigmented Skin Lesions and Melanoma Diagnosis”
[6] .I. Maglogiannis And C. N. Doukas, “Overview Of Advanced Computer Vision Systems For Skin Lesions Characterization,” Ieee Transactions On Information Technology In Biomedicine, Vol. 13, No.,Pp.721–733,2009.
[7] Ruben Nicolas,1 Albert Fornells,” DERMA: A Melanoma Diagnosis Platform Based onv Collaborative MultilabelAnalog Reasoning”.
[8] Cortes, Corinna, Vapnik and Vladimir N, “Support Vector Networks, Machine Learning”.
[9] Xin Li and YuhongGuo “Active Learning with Multi-Label SVM Classification” Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence.
[10] Andreas Vlachos,” Active Learning with Support Vector Machines
[11] GLENN M. FUNG,” Multicategory Proximal Support Vector Machine Classifiers” 2005 Springer Science + Business Media, Inc. Manufactured in The Netherlands.
[12] C.-W. Hsu and C.-J. Lin. A comparison of methods for multi-classsupport vector machines. IEEE Transactions on Neural Networks,2002.]
[13] Kapoor, K. Grauman, R. Urtasun, and T. Darrell. Active learningwith Gaussian Processes for object categorization. In ICCV, 2007.
[14] M. Li and I. Sethi. Confidence-based active learning. IEEE Trans.PAMI, 2006.
[15] H.-T. Lin, C.-J. Lin, and R. C. Weng. A note on Platt’s probabilisticoutputs for support vector machines. Machine Learning, 2007.
[16] T. Mitchell. Machine Learning. Boston: McGraw-Hill, 1997.
[17] A. Dhawan. “An expert system for the early detection of melanoma using knowledge-based image analysis.” Anal Quant CytolHistol10, pp. 405–416, 1988.
[18] D. Gutkowicz-Krusin, M. Elbaum, P. Szwaykowski et al. “Can early malignant melanoma be differentiated from atypicalmelanocytic nevus by in vivo techniques?” Skin Res Technolpp. 3:15–22, 199.
[19] S. McDonagh. “Skin Cancer Surface Based Classification.” Undergraduate Thesis, School of Informatics, University of Edinburgh2008.
[20] H. Ganster, A. Pinz, R. Rohrer et al. “Automated melanoma recognition.” IEEE Transactions on Medical Imaging 20, pp. 234–239, 2001.
Cite This Article
  • APA Style

    Immagulate I., Vijaya M. S. (2015). Categorization of Non-Melanoma Skin Lesion Diseases Using Support Vector Machine and Its Variants. International Journal of Medical Imaging, 3(2), 34-40. https://doi.org/10.11648/j.ijmi.20150302.15

    Copy | Download

    ACS Style

    Immagulate I.; Vijaya M. S. Categorization of Non-Melanoma Skin Lesion Diseases Using Support Vector Machine and Its Variants. Int. J. Med. Imaging 2015, 3(2), 34-40. doi: 10.11648/j.ijmi.20150302.15

    Copy | Download

    AMA Style

    Immagulate I., Vijaya M. S. Categorization of Non-Melanoma Skin Lesion Diseases Using Support Vector Machine and Its Variants. Int J Med Imaging. 2015;3(2):34-40. doi: 10.11648/j.ijmi.20150302.15

    Copy | Download

  • @article{10.11648/j.ijmi.20150302.15,
      author = {Immagulate I. and Vijaya M. S.},
      title = {Categorization of Non-Melanoma Skin Lesion Diseases Using Support Vector Machine and Its Variants},
      journal = {International Journal of Medical Imaging},
      volume = {3},
      number = {2},
      pages = {34-40},
      doi = {10.11648/j.ijmi.20150302.15},
      url = {https://doi.org/10.11648/j.ijmi.20150302.15},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmi.20150302.15},
      abstract = {Skin cancer is the growth of uncontrolled abnormal skin cells. There are two main types of skin cancers such as Melanoma and Non-Melanoma. The main objective of this research work is to focus on Non-Melanoma skin cancers and classify the types of it.The classification of non melanoma skin cancers is automated using machine learning approach and the model is built to predict the type of disease accurately using support vector machine and its variants. Various experiments have been carried out with skin lesion images and the results are analyzed.},
     year = {2015}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Categorization of Non-Melanoma Skin Lesion Diseases Using Support Vector Machine and Its Variants
    AU  - Immagulate I.
    AU  - Vijaya M. S.
    Y1  - 2015/03/18
    PY  - 2015
    N1  - https://doi.org/10.11648/j.ijmi.20150302.15
    DO  - 10.11648/j.ijmi.20150302.15
    T2  - International Journal of Medical Imaging
    JF  - International Journal of Medical Imaging
    JO  - International Journal of Medical Imaging
    SP  - 34
    EP  - 40
    PB  - Science Publishing Group
    SN  - 2330-832X
    UR  - https://doi.org/10.11648/j.ijmi.20150302.15
    AB  - Skin cancer is the growth of uncontrolled abnormal skin cells. There are two main types of skin cancers such as Melanoma and Non-Melanoma. The main objective of this research work is to focus on Non-Melanoma skin cancers and classify the types of it.The classification of non melanoma skin cancers is automated using machine learning approach and the model is built to predict the type of disease accurately using support vector machine and its variants. Various experiments have been carried out with skin lesion images and the results are analyzed.
    VL  - 3
    IS  - 2
    ER  - 

    Copy | Download

Author Information
  • PSGR Krishnammal College for Women, Coimbatore, India

  • PSGR Krishnammal College for Women, Coimbatore, India

  • Sections