A Dual Phase Probabilistic Model for Dermatology Classification

  • ShwetaEmail author
  • Sangeeta Rani
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 5)


Medical data processing is one of the most required and critical application areas. The work requires the expert information processing with each stage. The data collection to classification must be defined with specified rule formulation and observations. In this paper, a specialized skin disease processing method is defined for Dermatology Disease. The proposed work model is divided in three main stages. In first stage, the data processing and analysis is applied under statistical parameters. In second stage, these parameters are observed with probabilistic measures to generate the predictive cell structure. In the final stage, the Bayesian network is applied to perform the disease classification. The implementation of work is applied on three different sample sets. The implementation results show that the method has provided the highly accurate results.


Dermatology Skin disease Classifier Bayesian net 


  1. 1.
    E. Weitschek, G. Felici and P. Bertolazzi, “Clinical Data Mining: Problems, Pitfalls and Solutions,” Database and Expert Systems Applications (DEXA), 2013 24th International Workshop on, Los Alamitos, CA, 2013, pp. 90–94.Google Scholar
  2. 2.
    N. Rajkumar and P. Jaganathan, “A new RBF kernel based learning method applied to multiclass dermatology diseases classification,” Information & Communication Technologies (ICT), 2013 IEEE Conference on, JeJu Island, 2013, pp. 551–556.Google Scholar
  3. 3.
    S. Abdul-Rahman, Ahmad Khairil Norhan, M. Yusoff, A. Mohamed and S. Mutalib, “Dermatology diagnosis with feature selection methods and artificial neural network,” Biomedical Engineering and Sciences (IECBES), 2012 IEEE EMBS Conference on, Langkawi, 2012, pp. 371–376.Google Scholar
  4. 4.
    A. Azzini and S. Marrara, “Dermatology Disease Classification via Novel Evolutionary Artificial Neural Network,” Database and Expert Systems Applications, 2007. DEXA ‘07. 18th International Workshop on, Regensburg, 2007, pp. 148–152.Google Scholar
  5. 5.
    Zhongyang Xiong, Yufang Zhang, Lei Zhang and Shujie Niu, “A Parallel Classification Algorithm Based on Hybrid Genetic Algorithm,” Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on, Dalian, 2006, pp. 3237–3240.Google Scholar
  6. 6.
    S. Chakravarty and P. Mohapatra, “Multi-class classification using Cuckoo Search based hybrid network,” 2015 IEEE Power, Communication and Information Technology Conference (PCITC), Bhubaneswar, India, 2015, pp. 953–960.Google Scholar
  7. 7.
    A. S. Barreto, “Multivariate statistical analysis for dermatological disease diagnosis,” Biomedical and Health Informatics (BHI), 2014 IEEE-EMBS International Conference on, Valencia, 2014, pp. 500–504.Google Scholar
  8. 8.
    C. Iyakaremye, P. Luukka and D. Koloseni, “Feature selection using Yu’s similarity measure and fuzzy entropy measures,” Fuzzy Systems (FUZZ-IEEE), 2012 IEEE International Conference on, Brisbane, QLD, 2012, pp. 1–6.Google Scholar
  9. 9.
    M. Maragoudakis and I. Maglogiannis, “Skin lesion diagnosis from images using novel ensemble classification techniques,” Information Technology and Applications in Biomedicine (ITAB), 2010 10th IEEE International Conference on, Corfu, 2010, pp. 1–5.Google Scholar
  10. 10.
    N. J. Dhinagar and M. Celenk, “Power spectra based classification of cancerous nevoscope skin images,” Computer Applications and Industrial Electronics (ICCAIE), 2011 IEEE International Conference on, Penang, 2011, pp. 278–283.Google Scholar
  11. 11.
    J. C. Riaño-Rojas, F. A. Prieto-Ortiz, L. J. Morantes, E. Sánchez-Camperos and F. Jaramillo-Ayerbe, “Segmentation and Extraction of Morphologic Features from Capillary Images,” Artificial Intelligence - Special Session, 2007. MICAI 2007. Sixth Mexican International Conference on, Aguascallentes, 2007, pp. 148–159.Google Scholar
  12. 12.
    I. Maglogiannis and C. N. Doukas, “Overview of Advanced Computer Vision Systems for Skin Lesions Characterization,” in IEEE Transactions on Information Technology in Biomedicine, vol. 13, no. 5, pp. 721–733, Sept. 2009.Google Scholar
  13. 13.
    M. V. Fidelis, H. S. Lopes and A. A. Freitas, “Discovering comprehensible classification rules with a genetic algorithm,” Evolutionary Computation, 2000. Proceedings of the 2000 Congress on, La Jolla, CA, 2000, pp. 805–810 vol. 1.Google Scholar
  14. 14.
    S. N. N. Alfisahrin and T. Mantoro, “Data Mining Techniques for Optimization of Liver Disease Classification,” Advanced Computer Science Applications and Technologies (ACSAT), 2013 International Conference on, Kuching, 2013, pp. 379–384.Google Scholar
  15. 15.
    S. Ranganatha, H. R. P. Raj, C. Anusha and S. K. Vinay, “Medical data mining and analysis for heart disease dataset using classification techniques,” Research & Technology in the Coming Decades (CRT 2013), National Conference on Challenges in, Ujire, 2013, pp. 1–5.Google Scholar

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© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringAmity UniversityNoidaIndia

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