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Classification of Dermatological Data with Self Organizing Maps and Support Vector Machine

Year 2019, Volume: 19 Issue: 3, 894 - 901, 31.12.2019
https://doi.org/10.35414/akufemubid.591816

Abstract

The
frequency incidence of dermatological diseases is increasing in parallel with
the fact that human skin is exposed to different chemicals.
Examined many skin diseases, many of them are
similar in shape and appearance, although the reasons for their appearance are
different.

In dermatology, the
differential diagnosis of Erythemato-squamous diseases is frequently
encountered by doctors.
Doctors
try to differentiate and diagnose diseases by evaluating clinical findings and
histopathological parameters together.
Many
researchers have developed different algorithms on the classification and
clustering of diseases and data that have been diagnosed from the UCI database.
In the present study, unlike previous studies, clinical and histopathological
findings of 6 different Erythamo Squamos skin diseases were clustered by
applying to SOM network separately.
As
a result of this clustering process, it is determined that Psoriasis - Cronic
Dermatitis and Seborreic Dermatitis - Pitriasis Rosea diseases were found in
the same cluster and the diagnoses are confused.
In order to prevent this confusion, clinical and
histopathological findings of the diseases were clustered by SOM method.
Clustering parameters of clinical and
histopathological findings were classified with SVM.
As a result of the study, it was achieved that
the classification of Psoriasis - Cronic Dermatitis diseases was classified as
0.89 with an accuracy of 0.93 and that of Seborreic Dermatitis - Pitriasis
Rosea with an accuracy of 0.79 and 0.80.

References

  • Abdel-Aal, R. E., et al. (2006). "Improving the classification of multiple disorders with problem decomposition." Journal of biomedical informatics 39(6): 612-625.
  • Abdel-Aal, R. E., et al. (2006). "Improving the classification of multiple disorders with problem decomposition." Journal of biomedical informatics 39(6): 612-625.
  • Fidan, U., et al. (2016). Clustering and classification of dermatologic data with Self Organization Map (SOM) method. 2016 Medical Technologies National Congress (TIPTEKNO), IEEE.
  • Haryanto, H., et al. (2015). "The Erythemato-Squamous Dermatology Diseases Severity Determination using Self-Organizing Map." IPTEK Journal of Proceedings Series 1(1).
  • Haykin, S. S., et al. (2009). Neural networks and learning machines, Pearson Upper Saddle River.
  • Karabatak, M. and M. C. Ince (2009). "A new feature selection method based on association rules for diagnosis of erythemato-squamous diseases." Expert Systems with Applications 36(10): 12500-12505.
  • Karaca, Y., et al. (2018). Classification of Erythematous-Squamous Skin Diseases Through SVM Kernels and Identification of Features with 1-D Continuous Wavelet Coefficient. International Conference on Computational Science and Its Applications, Springer.
  • Kohonen, T. (1982). "Self-organized formation of topologically correct feature maps." Biological cybernetics 43(1): 59-69.
  • Küçüksille, E. and N. Ateş "Destek Vektör Makineleri ile Yaramaz Elektronik Postaların Filtrelenmesi." Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi 6(1).
  • Nanni, L. (2006). "An ensemble of classifiers for the diagnosis of erythemato-squamous diseases." Neurocomputing 69(7-9): 842-845.
  • Nouri, F. and N. S. Şengör "Öz-düzenlemeli Ağ Yapısı ile Farklı Yaklaşımların Sınanması Testing Different Approaches by Self Organizing Map."
  • Ozcift, A. and A. Gulten (2012). "A robust multi-class feature selection strategy based on rotation forest ensemble algorithm for diagnosis of erythemato-squamous diseases." Journal of medical systems 36(2): 941-949.
  • Übeyli, E. D. (2008). "Multiclass support vector machines for diagnosis of erythemato-squamous diseases." Expert Systems with Applications 35(4): 1733-1740.
  • Übeyli, E. D. and E. Doğdu (2010). "Automatic detection of erythemato-squamous diseases using k-means clustering." Journal of medical systems 34(2): 179-184.
  • Übeylı, E. D. and I. Güler (2005). "Automatic detection of erythemato-squamous diseases using adaptive neuro-fuzzy inference systems." Computers in biology and medicine 35(5): 421-433.
  • West, D. and V. West (2000). "Improving diagnostic accuracy using a hierarchical neural network to model decision subtasks." International journal of medical informatics 57(1): 41-55.
  • Xie, J. and C. Wang (2011). "Using support vector machines with a novel hybrid feature selection method for diagnosis of erythemato-squamous diseases." Expert Systems with Applications 38(5): 5809-5815.

Dermatolojik Verilerin Öz Düzenleyici Harita ve Destek Vektör Makinaları ile Sınıflandırılması

Year 2019, Volume: 19 Issue: 3, 894 - 901, 31.12.2019
https://doi.org/10.35414/akufemubid.591816

Abstract

İnsan derisinin özellikle
farklı kimyasallara maruz kaldığı günümüzde dermatolojik hastalıkların görülme
sıklığı da buna paralel olarak artış göstermektedir. Birçok deri hastalığı
incelendiğinde birçoğu ortaya çıkış sebepleri farklı olmasına karşın şekil ve
görünüş açısından benzerlik taşımaktadır. Dermatolojide, Erythemato-squamos
hastalıklarına ayırt edici tanı koyulması doktorların sıkça karşılaştığı bir
durumdur. Doktorlar klinik bulgular ile histopatolojik parametreleri birlikte
değerlendirerek hastalıkları birbirinden ayırt etmeye ve teşhis koymaya
çalışmaktadır. Konu ile ilgili birçok araştırmacı UCI veri tabanından alınan ve
tanısını konmuş veriler ile hastalıkların sınıflandırılması ve kümelenmesi
üzerine farklı algoritmalar geliştirmiştir. Bu çalışmada önceki çalışmalardan
farklı olarak 6 farklı Erythamo Squamos deri hastalığına ait klinik ve
histopatolojik bulgular SOM ağına ayrı ayrı uygulanarak kümelenmiştir. Bu
kümeleme işleminin sonucunda  Psoriasis -
Cronic Dermatitis ve Seborreic Dermatitis - Pitriasis Rosea hastalıkları aynı
küme içerisinde kaldığı ve tanıların karıştırıldığı tespit edilmiştir. Bu
karışmayı önlemek için hastalıkların klinik ve histopatolojik bulguları ayrı
ayrı SOM yöntemi ile kümelenmiştir. Klinik ve histopatolojik bulgulara ait
kümelenme parametreleri kullanılarak SVM ile sınıflandırılma yapılmıştır.
Yapılan çalışma sonucunda karıştırılan Psoriasis - Cronic Dermatitis
hastalıkları arasında F1 sokuru 0.89 doğruluğu 0.93 olarak ve Seborreic
Dermatitis - Pitriasis Rosea hastalıkları arasında F1 sokuru 0.79 doğruluğu
0.80 olarak sınıflandırma başarımı sağlanmıştır.

References

  • Abdel-Aal, R. E., et al. (2006). "Improving the classification of multiple disorders with problem decomposition." Journal of biomedical informatics 39(6): 612-625.
  • Abdel-Aal, R. E., et al. (2006). "Improving the classification of multiple disorders with problem decomposition." Journal of biomedical informatics 39(6): 612-625.
  • Fidan, U., et al. (2016). Clustering and classification of dermatologic data with Self Organization Map (SOM) method. 2016 Medical Technologies National Congress (TIPTEKNO), IEEE.
  • Haryanto, H., et al. (2015). "The Erythemato-Squamous Dermatology Diseases Severity Determination using Self-Organizing Map." IPTEK Journal of Proceedings Series 1(1).
  • Haykin, S. S., et al. (2009). Neural networks and learning machines, Pearson Upper Saddle River.
  • Karabatak, M. and M. C. Ince (2009). "A new feature selection method based on association rules for diagnosis of erythemato-squamous diseases." Expert Systems with Applications 36(10): 12500-12505.
  • Karaca, Y., et al. (2018). Classification of Erythematous-Squamous Skin Diseases Through SVM Kernels and Identification of Features with 1-D Continuous Wavelet Coefficient. International Conference on Computational Science and Its Applications, Springer.
  • Kohonen, T. (1982). "Self-organized formation of topologically correct feature maps." Biological cybernetics 43(1): 59-69.
  • Küçüksille, E. and N. Ateş "Destek Vektör Makineleri ile Yaramaz Elektronik Postaların Filtrelenmesi." Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi 6(1).
  • Nanni, L. (2006). "An ensemble of classifiers for the diagnosis of erythemato-squamous diseases." Neurocomputing 69(7-9): 842-845.
  • Nouri, F. and N. S. Şengör "Öz-düzenlemeli Ağ Yapısı ile Farklı Yaklaşımların Sınanması Testing Different Approaches by Self Organizing Map."
  • Ozcift, A. and A. Gulten (2012). "A robust multi-class feature selection strategy based on rotation forest ensemble algorithm for diagnosis of erythemato-squamous diseases." Journal of medical systems 36(2): 941-949.
  • Übeyli, E. D. (2008). "Multiclass support vector machines for diagnosis of erythemato-squamous diseases." Expert Systems with Applications 35(4): 1733-1740.
  • Übeyli, E. D. and E. Doğdu (2010). "Automatic detection of erythemato-squamous diseases using k-means clustering." Journal of medical systems 34(2): 179-184.
  • Übeylı, E. D. and I. Güler (2005). "Automatic detection of erythemato-squamous diseases using adaptive neuro-fuzzy inference systems." Computers in biology and medicine 35(5): 421-433.
  • West, D. and V. West (2000). "Improving diagnostic accuracy using a hierarchical neural network to model decision subtasks." International journal of medical informatics 57(1): 41-55.
  • Xie, J. and C. Wang (2011). "Using support vector machines with a novel hybrid feature selection method for diagnosis of erythemato-squamous diseases." Expert Systems with Applications 38(5): 5809-5815.
There are 17 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Uğur Fidan 0000-0003-0356-017X

Esma Uzunhisarcıklı This is me 0000-0003-2821-4177

İsmail Çalıkuşu This is me 0000-0002-6640-7917

Publication Date December 31, 2019
Submission Date July 14, 2019
Published in Issue Year 2019 Volume: 19 Issue: 3

Cite

APA Fidan, U., Uzunhisarcıklı, E., & Çalıkuşu, İ. (2019). Classification of Dermatological Data with Self Organizing Maps and Support Vector Machine. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, 19(3), 894-901. https://doi.org/10.35414/akufemubid.591816
AMA Fidan U, Uzunhisarcıklı E, Çalıkuşu İ. Classification of Dermatological Data with Self Organizing Maps and Support Vector Machine. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. December 2019;19(3):894-901. doi:10.35414/akufemubid.591816
Chicago Fidan, Uğur, Esma Uzunhisarcıklı, and İsmail Çalıkuşu. “Classification of Dermatological Data With Self Organizing Maps and Support Vector Machine”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 19, no. 3 (December 2019): 894-901. https://doi.org/10.35414/akufemubid.591816.
EndNote Fidan U, Uzunhisarcıklı E, Çalıkuşu İ (December 1, 2019) Classification of Dermatological Data with Self Organizing Maps and Support Vector Machine. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 19 3 894–901.
IEEE U. Fidan, E. Uzunhisarcıklı, and İ. Çalıkuşu, “Classification of Dermatological Data with Self Organizing Maps and Support Vector Machine”, Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, vol. 19, no. 3, pp. 894–901, 2019, doi: 10.35414/akufemubid.591816.
ISNAD Fidan, Uğur et al. “Classification of Dermatological Data With Self Organizing Maps and Support Vector Machine”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 19/3 (December 2019), 894-901. https://doi.org/10.35414/akufemubid.591816.
JAMA Fidan U, Uzunhisarcıklı E, Çalıkuşu İ. Classification of Dermatological Data with Self Organizing Maps and Support Vector Machine. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. 2019;19:894–901.
MLA Fidan, Uğur et al. “Classification of Dermatological Data With Self Organizing Maps and Support Vector Machine”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, vol. 19, no. 3, 2019, pp. 894-01, doi:10.35414/akufemubid.591816.
Vancouver Fidan U, Uzunhisarcıklı E, Çalıkuşu İ. Classification of Dermatological Data with Self Organizing Maps and Support Vector Machine. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. 2019;19(3):894-901.