| Peer-Reviewed

Applying Different Pattern Recognition Methods for Identifying Skin Diseases

Received: 25 October 2020     Accepted: 10 November 2020     Published: 19 November 2020
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Abstract

According to the WHO (World Health Organization) (2015), cancer is the first or second major cause of death before the age of 70 in 91 of 172 countries, and it is ranked third or fourth in 22 other countries. In 2018, out of 1042056 new non-melanoma skin cancer cases in the world, 6.25% of them had been reported to have died. The most effective method to reduce disease mortality is early diagnosis, which requires a precision and reliable diagnosis. Automatic diagnosis is speedy and far from human error and reduces the workload and warns about patients who need more attention, and allows physicians to focus on diagnosis and prognosis. For automatic classification, six K-NN methods, weighted K-NN, Bayesian, perceptron artificial neural network, RBF neural network, SVM are used, and the results of the correct classification rate are compared. Then the correct classification rate is significantly increased using the FDR formula and genetic algorithm. RBF, perceptron artificial neural network, and weighted K-NN methods had the best precision of classification, respectively. After applying the genetic coefficients, RBF weighted K-NN and K-NN methods are reached to a precision of 100%. After them, SVM and perceptron artificial neural network methods are reached to a precision of 99%.

Published in Machine Learning Research (Volume 5, Issue 3)
DOI 10.11648/j.mlr.20200503.11
Page(s) 39-45
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), 2020. Published by Science Publishing Group

Keywords

Neural Network, RBF, Perceptron, K-NN, Bayesian, Melanoma, Eczema, Psoriasis, Genetic Algorithm, FDR

References
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Cite This Article
  • APA Style

    Amir Zirjam, Saman Rajebi. (2020). Applying Different Pattern Recognition Methods for Identifying Skin Diseases. Machine Learning Research, 5(3), 39-45. https://doi.org/10.11648/j.mlr.20200503.11

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    ACS Style

    Amir Zirjam; Saman Rajebi. Applying Different Pattern Recognition Methods for Identifying Skin Diseases. Mach. Learn. Res. 2020, 5(3), 39-45. doi: 10.11648/j.mlr.20200503.11

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    AMA Style

    Amir Zirjam, Saman Rajebi. Applying Different Pattern Recognition Methods for Identifying Skin Diseases. Mach Learn Res. 2020;5(3):39-45. doi: 10.11648/j.mlr.20200503.11

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  • @article{10.11648/j.mlr.20200503.11,
      author = {Amir Zirjam and Saman Rajebi},
      title = {Applying Different Pattern Recognition Methods for Identifying Skin Diseases},
      journal = {Machine Learning Research},
      volume = {5},
      number = {3},
      pages = {39-45},
      doi = {10.11648/j.mlr.20200503.11},
      url = {https://doi.org/10.11648/j.mlr.20200503.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20200503.11},
      abstract = {According to the WHO (World Health Organization) (2015), cancer is the first or second major cause of death before the age of 70 in 91 of 172 countries, and it is ranked third or fourth in 22 other countries. In 2018, out of 1042056 new non-melanoma skin cancer cases in the world, 6.25% of them had been reported to have died. The most effective method to reduce disease mortality is early diagnosis, which requires a precision and reliable diagnosis. Automatic diagnosis is speedy and far from human error and reduces the workload and warns about patients who need more attention, and allows physicians to focus on diagnosis and prognosis. For automatic classification, six K-NN methods, weighted K-NN, Bayesian, perceptron artificial neural network, RBF neural network, SVM are used, and the results of the correct classification rate are compared. Then the correct classification rate is significantly increased using the FDR formula and genetic algorithm. RBF, perceptron artificial neural network, and weighted K-NN methods had the best precision of classification, respectively. After applying the genetic coefficients, RBF weighted K-NN and K-NN methods are reached to a precision of 100%. After them, SVM and perceptron artificial neural network methods are reached to a precision of 99%.},
     year = {2020}
    }
    

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  • TY  - JOUR
    T1  - Applying Different Pattern Recognition Methods for Identifying Skin Diseases
    AU  - Amir Zirjam
    AU  - Saman Rajebi
    Y1  - 2020/11/19
    PY  - 2020
    N1  - https://doi.org/10.11648/j.mlr.20200503.11
    DO  - 10.11648/j.mlr.20200503.11
    T2  - Machine Learning Research
    JF  - Machine Learning Research
    JO  - Machine Learning Research
    SP  - 39
    EP  - 45
    PB  - Science Publishing Group
    SN  - 2637-5680
    UR  - https://doi.org/10.11648/j.mlr.20200503.11
    AB  - According to the WHO (World Health Organization) (2015), cancer is the first or second major cause of death before the age of 70 in 91 of 172 countries, and it is ranked third or fourth in 22 other countries. In 2018, out of 1042056 new non-melanoma skin cancer cases in the world, 6.25% of them had been reported to have died. The most effective method to reduce disease mortality is early diagnosis, which requires a precision and reliable diagnosis. Automatic diagnosis is speedy and far from human error and reduces the workload and warns about patients who need more attention, and allows physicians to focus on diagnosis and prognosis. For automatic classification, six K-NN methods, weighted K-NN, Bayesian, perceptron artificial neural network, RBF neural network, SVM are used, and the results of the correct classification rate are compared. Then the correct classification rate is significantly increased using the FDR formula and genetic algorithm. RBF, perceptron artificial neural network, and weighted K-NN methods had the best precision of classification, respectively. After applying the genetic coefficients, RBF weighted K-NN and K-NN methods are reached to a precision of 100%. After them, SVM and perceptron artificial neural network methods are reached to a precision of 99%.
    VL  - 5
    IS  - 3
    ER  - 

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Author Information
  • Department of Biomedical Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran

  • Faculty of Electrical Engineering, Siraj Institute of Higher Education, Tabriz, Iran

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