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Year 2021, Volume: 9 Issue: 2, 324 - 331, 15.10.2021

Murat Kirisci İbrahim Demir Necip Şimşek

Abstract

References

  • [1] M.T.Hagan,M.B.Menhaj,TrainingfeedforwardnetworkswiththeMarquardtalgorithm.IEEETrans.NeuralNetw.,5(1994),989–993.
  • [2] MuratKayri,PredictiveAbilitiesofBayesianRegularizationandLevenberg–MarquardtAlgorithmsinArtificialNeuralNetworks:AComparative Empirical Study on Social Data, Math. Comput. Appl. 21, (2016), 20, DOI:10.3390/mca21020020
  • [3] R.Dybowski,V.Gant,ClinicalApplicationsofArtificialNeuralNetworks,CambridgeUniversityPress,2007.
  • [4] V. Arbabi, B. Pouran, G. Campoli, H. Weinans, A.A. Zadpoor, Determination of the mechanical and physical properties of cartilage by coupling poroelastic-based finite element models of indentation with artificial neural networks, J Biomech. 49, (2016), ,631–637.
  • [5] J.Dolz,L.Massoptier,M.Vermandel,SegmentationalgorithmsofsubcorticalbrainstructuresonMRIforradiotherapyandradiosurgery:asurvey, IRBM, 36(4), (2015), 200–212.
  • [6] O.Er,N.Yumusak,F.Temurtas,Chestdiseasediagnosisusingartificialneuralnetworks,ExpertSystemswithApplications,37,(2010),7648–7655.
  • [7] O. Er, F. Temurtas, A.C. Tanrikulu, Tuberculosis Disease Diagnosis Using Artificial Neural Networks, J Med Syst, 34, (2010), 299–302, DOI: 10.1007/s10916-008-9241-x
  • [8] J.M.Haglin,G.Jimenez,A.E.M.Eltorai,Artificialneuralnetworksinmedicine,HealthandTechnology,9,(2019),1–6,DOI:10.1007/s12553-018- 0244-4
  • [9] M. Kirisci, H. Yilmaz, M.U. Saka, An ANFIS perspective for the diagnosis of type II diabetes, Annals of Fuzzy Mathematics and Informatics, 17, (2019), 101–113, DOI: 10.30948/afmi.2019.17.2.101
  • [10] M.Kirisci,Comparisonofartificialneuralnetworkandlogisticregressionmodelforfactorsaffectingbirthweight,SNAppl.Sci.,1,(2019),378,DOI: 10.1007/s42452-019-0391-x
  • [11] M. Kirisci, H. Yilmaz, Artificial Neural Networks for Analysis of Factors Affecting Birth Weight, American Journal of Information Science and Computer Engineering, 5, (2019), 17–24.
  • [12] J.Kojuri,R.Boostani,P.Dehghani,F.Nowroozipour,N.Saki,Predictionofacutemyocardialinfarctionwithartificialneuralnetworksinpatientswith nondiagnostic electrocardiogram, Journal of Cardiovascular Disease Research, 6(2), (2015), 51–59.
  • [13] C.C.Lin,Y.K.Ou,S.H.Chen,Y.C.Liu,J.Lin,Comparisonofartificialneuralnetworkandlogisticregressionmodelsforpredictingmortalityinelderly patients with hip fracture, Injury. 41(9), (2010), 869–873.
  • [14] I.M.Nasser,S.S.Abu-Naser,PredictingTumorCategoryUsingArtificialNeuralNetworks,InternationalJournalofAcademicHealthandMedical Research, 3, (2019), 1–7.
  • [15] T.Nowikiewicz,P.Wnuk,B.Małkowski,A.Kurylcio,J.Kowalewski,W.Zegarski,Applicationofartificialneuralnetworksforpredictingpresenceof non-sentinel lymph node metastases in breast cancer patients with positive sentinel lymph node biopsies, Archives of Medical Science, 13, (2017), 1399-–1407.
  • [16] M.Shioji,T.Yamamoto,T.Ibata,T.Tsuda,K.Adachi,N.Yoshimura,Artificialneuralnetworkstopredictfuturebonemineraldensityandboneloss rate in Japanese postmenopausal women, BMC Research Notes, 10, (2017), 590.
  • [17] H. Yu, D.C. Samuels, Y.Y. Zhao, Y. Guo, Architectures and accuracy of artificial neural network for disease classification from omics data, BMC Genomics, 20, (2019), 167, DOI:10.1186/s12864-019-5546-z
  • [18] W.S.McCulloch,W.Pitts,Alogicalcalculusoftheideasimmanentinnervousactivity,BulletinofMath.Biophysics,5,(1943),115–133.
  • [19] J.j.Hopfield,Neuralnetworksandphysicalsystemswithemergentcollectivecomputationalabilities,Proc.Nat.Acad.Sci.,79,(1982),2554–2558.
  • [20] T.Kohonen,Self-organisedformationoftopologicallycorrectfeaturemaps,BiologicalCybernetics,43,(1982),59–69.

Comparative Analysis of Neural Networks in the Diagnosis of Emerging Diseases based on COVID-19

Year 2021, Volume: 9 Issue: 2, 324 - 331, 15.10.2021

Murat Kirisci İbrahim Demir Necip Şimşek

Abstract

Dermatological diseases are frequently encountered in children and adults for various reasons. There are many factors that cause the onset of these diseases and different symptoms are generally seen in each age group.
Artificial neural networks can provide expert-level accuracy in the diagnosis of dermatological findings of patients with COVID-19 disease. Therefore, the use of neural network classification methods can give the best estimation method in dermatology. In this study, the prediction of cutaneous diseases caused by COVID-19 was analyzed by Scaled Conjugate Gradient, Levenberg Marquardt, Bayesian Regularization neural networks. At some points, Bayesian Regularization and Levenberg Marquardt were almost equally effective, but Bayesian Regularization performed better than Levenberg Marquard and called Conjugate Gradient in performance. It is seen that neural network model predictions achieve the highest accuracy. For this reason, artificial neural networks are able to classify these diseases as accurately as human experts in an experimental setting.

Keywords

Bayesian Regularization Neural Network COVID-19 dermatological findings Levenberg–Marquardt Neural Network Scaled Conjugate Gradient Neural Network

References

  • [1] M.T.Hagan,M.B.Menhaj,TrainingfeedforwardnetworkswiththeMarquardtalgorithm.IEEETrans.NeuralNetw.,5(1994),989–993.
  • [2] MuratKayri,PredictiveAbilitiesofBayesianRegularizationandLevenberg–MarquardtAlgorithmsinArtificialNeuralNetworks:AComparative Empirical Study on Social Data, Math. Comput. Appl. 21, (2016), 20, DOI:10.3390/mca21020020
  • [3] R.Dybowski,V.Gant,ClinicalApplicationsofArtificialNeuralNetworks,CambridgeUniversityPress,2007.
  • [4] V. Arbabi, B. Pouran, G. Campoli, H. Weinans, A.A. Zadpoor, Determination of the mechanical and physical properties of cartilage by coupling poroelastic-based finite element models of indentation with artificial neural networks, J Biomech. 49, (2016), ,631–637.
  • [5] J.Dolz,L.Massoptier,M.Vermandel,SegmentationalgorithmsofsubcorticalbrainstructuresonMRIforradiotherapyandradiosurgery:asurvey, IRBM, 36(4), (2015), 200–212.
  • [6] O.Er,N.Yumusak,F.Temurtas,Chestdiseasediagnosisusingartificialneuralnetworks,ExpertSystemswithApplications,37,(2010),7648–7655.
  • [7] O. Er, F. Temurtas, A.C. Tanrikulu, Tuberculosis Disease Diagnosis Using Artificial Neural Networks, J Med Syst, 34, (2010), 299–302, DOI: 10.1007/s10916-008-9241-x
  • [8] J.M.Haglin,G.Jimenez,A.E.M.Eltorai,Artificialneuralnetworksinmedicine,HealthandTechnology,9,(2019),1–6,DOI:10.1007/s12553-018- 0244-4
  • [9] M. Kirisci, H. Yilmaz, M.U. Saka, An ANFIS perspective for the diagnosis of type II diabetes, Annals of Fuzzy Mathematics and Informatics, 17, (2019), 101–113, DOI: 10.30948/afmi.2019.17.2.101
  • [10] M.Kirisci,Comparisonofartificialneuralnetworkandlogisticregressionmodelforfactorsaffectingbirthweight,SNAppl.Sci.,1,(2019),378,DOI: 10.1007/s42452-019-0391-x
  • [11] M. Kirisci, H. Yilmaz, Artificial Neural Networks for Analysis of Factors Affecting Birth Weight, American Journal of Information Science and Computer Engineering, 5, (2019), 17–24.
  • [12] J.Kojuri,R.Boostani,P.Dehghani,F.Nowroozipour,N.Saki,Predictionofacutemyocardialinfarctionwithartificialneuralnetworksinpatientswith nondiagnostic electrocardiogram, Journal of Cardiovascular Disease Research, 6(2), (2015), 51–59.
  • [13] C.C.Lin,Y.K.Ou,S.H.Chen,Y.C.Liu,J.Lin,Comparisonofartificialneuralnetworkandlogisticregressionmodelsforpredictingmortalityinelderly patients with hip fracture, Injury. 41(9), (2010), 869–873.
  • [14] I.M.Nasser,S.S.Abu-Naser,PredictingTumorCategoryUsingArtificialNeuralNetworks,InternationalJournalofAcademicHealthandMedical Research, 3, (2019), 1–7.
  • [15] T.Nowikiewicz,P.Wnuk,B.Małkowski,A.Kurylcio,J.Kowalewski,W.Zegarski,Applicationofartificialneuralnetworksforpredictingpresenceof non-sentinel lymph node metastases in breast cancer patients with positive sentinel lymph node biopsies, Archives of Medical Science, 13, (2017), 1399-–1407.
  • [16] M.Shioji,T.Yamamoto,T.Ibata,T.Tsuda,K.Adachi,N.Yoshimura,Artificialneuralnetworkstopredictfuturebonemineraldensityandboneloss rate in Japanese postmenopausal women, BMC Research Notes, 10, (2017), 590.
  • [17] H. Yu, D.C. Samuels, Y.Y. Zhao, Y. Guo, Architectures and accuracy of artificial neural network for disease classification from omics data, BMC Genomics, 20, (2019), 167, DOI:10.1186/s12864-019-5546-z
  • [18] W.S.McCulloch,W.Pitts,Alogicalcalculusoftheideasimmanentinnervousactivity,BulletinofMath.Biophysics,5,(1943),115–133.
  • [19] J.j.Hopfield,Neuralnetworksandphysicalsystemswithemergentcollectivecomputationalabilities,Proc.Nat.Acad.Sci.,79,(1982),2554–2558.
  • [20] T.Kohonen,Self-organisedformationoftopologicallycorrectfeaturemaps,BiologicalCybernetics,43,(1982),59–69.
There are 20 citations in total.

Details

Primary Language English
Subjects Applied Mathematics
Journal Section Articles
Authors

Murat Kirisci

İbrahim Demir 0000-0002-2734-4116

Necip Şimşek

Publication Date October 15, 2021
Submission Date September 6, 2021
Acceptance Date September 13, 2021
Published in Issue Year 2021 Volume: 9 Issue: 2

Cite

APA Kirisci, M., Demir, İ., & Şimşek, N. (2021). Comparative Analysis of Neural Networks in the Diagnosis of Emerging Diseases based on COVID-19. Konuralp Journal of Mathematics, 9(2), 324-331.
AMA Kirisci M, Demir İ, Şimşek N. Comparative Analysis of Neural Networks in the Diagnosis of Emerging Diseases based on COVID-19. Konuralp J. Math. October 2021;9(2):324-331.
Chicago Kirisci, Murat, İbrahim Demir, and Necip Şimşek. “Comparative Analysis of Neural Networks in the Diagnosis of Emerging Diseases Based on COVID-19”. Konuralp Journal of Mathematics 9, no. 2 (October 2021): 324-31.
EndNote Kirisci M, Demir İ, Şimşek N (October 1, 2021) Comparative Analysis of Neural Networks in the Diagnosis of Emerging Diseases based on COVID-19. Konuralp Journal of Mathematics 9 2 324–331.
IEEE M. Kirisci, İ. Demir, and N. Şimşek, “Comparative Analysis of Neural Networks in the Diagnosis of Emerging Diseases based on COVID-19”, Konuralp J. Math., vol. 9, no. 2, pp. 324–331, 2021.
ISNAD Kirisci, Murat et al. “Comparative Analysis of Neural Networks in the Diagnosis of Emerging Diseases Based on COVID-19”. Konuralp Journal of Mathematics 9/2 (October 2021), 324-331.
JAMA Kirisci M, Demir İ, Şimşek N. Comparative Analysis of Neural Networks in the Diagnosis of Emerging Diseases based on COVID-19. Konuralp J. Math. 2021;9:324–331.
MLA Kirisci, Murat et al. “Comparative Analysis of Neural Networks in the Diagnosis of Emerging Diseases Based on COVID-19”. Konuralp Journal of Mathematics, vol. 9, no. 2, 2021, pp. 324-31.
Vancouver Kirisci M, Demir İ, Şimşek N. Comparative Analysis of Neural Networks in the Diagnosis of Emerging Diseases based on COVID-19. Konuralp J. Math. 2021;9(2):324-31.
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