Volume 5, Issue 3, September 2020, Page: 39-45
Applying Different Pattern Recognition Methods for Identifying Skin Diseases
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
Received: Oct. 25, 2020;       Accepted: Nov. 10, 2020;       Published: Nov. 19, 2020
DOI: 10.11648/j.mlr.20200503.11      View  20      Downloads  14
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%.
Keywords
Neural Network, RBF, Perceptron, K-NN, Bayesian, Melanoma, Eczema, Psoriasis, Genetic Algorithm, FDR
To cite this article
Amir Zirjam, Saman Rajebi, Applying Different Pattern Recognition Methods for Identifying Skin Diseases, Machine Learning Research. Vol. 5, No. 3, 2020, pp. 39-45. doi: 10.11648/j.mlr.20200503.11
Copyright
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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