Applying Different Pattern Recognition Methods for Identifying Skin Diseases

Amir Zirjam; Saman Rajebi

doi:doi:10.11648/j.mlr.20200503.11

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Applying Different Pattern Recognition Methods for Identifying Skin Diseases

Amir Zirjam, Saman Rajebi

Published in Machine Learning Research (Volume 5, Issue 3)

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), 2024. Published by Science Publishing Group

Keywords

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

References

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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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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

Amir Zirjam

Department of Biomedical Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
Saman Rajebi

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

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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

Copy | Download

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

Copy | Download

@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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