30 Temmuz 2024 Dergi makalesi Açık Erişim

Gülmez, Burak

JSON-LD (schema.org)

{
  "@context": "https://schema.org/", 
  "@id": 273782, 
  "@type": "ScholarlyArticle", 
  "creator": [
    {
      "@id": "https://orcid.org/0000-0002-6870-6558", 
      "@type": "Person", 
      "affiliation": "Leiden University", 
      "name": "G\u00fclmez, Burak"
    }
  ], 
  "datePublished": "2024-07-30", 
  "description": "<p><strong>A new multi-objective hyperparameter optimization algorithm for COVID-19 detection from x-ray images</strong></p>\n\n<p>Burak G&uuml;lmez</p>\n\n<p><a href=\"mailto:b.gulmez@liacs.leidenuniv.nl\">b.gulmez@liacs.leidenuniv.nl</a></p>\n\n<p>Leiden Institute of Advanced Computer Science, Leiden University, Leiden, Netherlands</p>\n\n<p><strong>Abstract</strong></p>\n\n<p>The coronavirus occurred in Wuhan (China) first and it was declared a global pandemic. To detect coronavirus X-ray images can be used. Convolutional neural networks (CNNs) are used commonly to detect illness from images. There can be lots of different alternative deep CNN models or architectures. To find the best architecture, hyper-parameter optimization can be used. In this study, the problem is modeled as a multi-objective optimization (MOO) problem. Objective functions are multi-class cross entropy, error ratio, and complexity of the CNN network. For the best solutions to the objective functions, multi-objective hyper-parameter optimization is made by NSGA-III, NSGA-II, R-NSGA-II, SMS-EMOA, MOEA/D, and proposed Swarm Genetic Algorithms (SGA). SGA is a swarm-based algorithm with a cross-over process. All six algorithms are run and give Pareto optimal solution sets. When the figures obtained from the algorithms are analyzed and algorithm hypervolume values are compared, SGA outperforms the NSGA-III, NSGA-II, R-NSGA-II, SMS-EMOA, and MOEA/D algorithms. It can be concluded that SGA is better than others for multi-objective hyper-parameter optimization algorithms for COVID-19 detection from X-ray images. Also, a sensitivity analysis has been made to understand the effect of the number of the parameters of CNN on model success.</p>\n\n<p><strong>References</strong></p>\n\n<p>Abdullah JM, Rashid TA, Maaroof BB, Mirjalili S (2023) Multi-objective fitness-dependent optimizer algorithm. Neural Comput Applic 35:11969&ndash;11987.&nbsp;<a href=\"https://doi.org/10.1007/s00521-023-08332-3\">https://doi.org/10.1007/s00521-023-08332-3</a></p>\n\n<p>Abraham A, Jain L (2005) Evolutionary multiobjective optimization. In: Abraham A, Jain L, Goldberg R (eds) Evolutionary Multiobjective optimization: theoretical advances and applications. Springer, London, pp 1&ndash;6</p>\n\n<p>Akingbesote D, Zhan Y, Maskeli\u016bnas R, Dama&scaron;evi\u010dius R (2023) Improving Accuracy of Face Recognition in the era of Mask-Wearing: an evaluation of a pareto-optimized FaceNet Model with Data Preprocessing techniques. Algorithms 16.&nbsp;<a href=\"https://doi.org/10.3390/a16060292\">https://doi.org/10.3390/a16060292</a></p>\n\n<p>Alhudhaif A, Polat K, Karaman O (2021) Determination of COVID-19 pneumonia based on generalized convolutional neural network model from chest X-ray images. Expert Syst Appl 180:115141.&nbsp;<a href=\"https://doi.org/10.1016/j.eswa.2021.115141\">https://doi.org/10.1016/j.eswa.2021.115141</a></p>\n\n<p>Beume N, Naujoks B, Emmerich M (2007) SMS-EMOA: multiobjective selection based on dominated hypervolume. Eur J Oper Res 181:1653&ndash;1669.&nbsp;<a href=\"https://doi.org/10.1016/j.ejor.2006.08.008\">https://doi.org/10.1016/j.ejor.2006.08.008</a></p>\n\n<p>Cheng VC-C, Wong S-C, Chuang VW-M et al (2020) The role of community-wide wearing of face mask for control of coronavirus disease 2019 (COVID-19) epidemic due to SARS-CoV-2. J Infect 81:107&ndash;114.&nbsp;<a href=\"https://doi.org/10.1016/j.jinf.2020.04.024\">https://doi.org/10.1016/j.jinf.2020.04.024</a></p>\n\n<p>Cho M, Kim Y (2021) FPGA-based convolutional neural network accelerator with resource-optimized approximate multiply-accumulate unit. Electronics 10:2859.&nbsp;<a href=\"https://doi.org/10.3390/electronics10222859\">https://doi.org/10.3390/electronics10222859</a></p>\n\n<p>&Ccedil;i\u011f H, G&uuml;ll&uuml;o\u011flu MT, Er MB et al (2023) Enhanced Disease Detection using contrast limited adaptive Histogram equalization and multi-objective cuckoo search in Deep Learning. Trait Signal 40:915&ndash;925.&nbsp;<a href=\"https://doi.org/10.18280/ts.400308\">https://doi.org/10.18280/ts.400308</a></p>\n\n<p>Covid-19 Image Dataset&nbsp;<a href=\"https://www.kaggle.com/datasets/pranavraikokte/covid19-image-dataset\">https://www.kaggle.com/datasets/pranavraikokte/covid19-image-dataset</a>. Accessed 15 Aug 2022</p>\n\n<p>Coxon K, Turienzo CF, Kweekel L et al (2020) The impact of the coronavirus (COVID-19) pandemic on maternity care in Europe. Midwifery 88:102779.&nbsp;<a href=\"https://doi.org/10.1016/j.midw.2020.102779\">https://doi.org/10.1016/j.midw.2020.102779</a></p>\n\n<p>Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6:182&ndash;197.&nbsp;<a href=\"https://doi.org/10.1109/4235.996017\">https://doi.org/10.1109/4235.996017</a></p>\n\n<p>Dhiman G, Vinoth Kumar V, Kaur A, Sharma A (2021) DON: deep learning and optimization-based Framework for detection of Novel Coronavirus Disease using X-ray images. Interdiscip Sci Comput Life Sci 13:260&ndash;272.&nbsp;<a href=\"https://doi.org/10.1007/s12539-021-00418-7\">https://doi.org/10.1007/s12539-021-00418-7</a></p>\n\n<p>Dhiman G, Chang V, Kant Singh K, Shankar A (2022) ADOPT: automatic deep learning and optimization-based approach for detection of novel coronavirus COVID-19 disease using X-ray images. J Biomol Struct Dyn 40:5836&ndash;5847.&nbsp;<a href=\"https://doi.org/10.1080/07391102.2021.1875049\">https://doi.org/10.1080/07391102.2021.1875049</a></p>\n\n<p>Filatovas E, Lan\u010dinskas A, Kurasova O, \u017dilinskas J (2017) A preference-based multi-objective evolutionary algorithm R-NSGA-II with stochastic local search. Cent Eur J Oper Res 25:859&ndash;878.&nbsp;<a href=\"https://doi.org/10.1007/s10100-016-0443-x\">https://doi.org/10.1007/s10100-016-0443-x</a></p>\n\n<p>Goel T, Murugan R, Mirjalili S, Chakrabartty DK (2022) Multi-COVID-Net: multi-objective optimized network for COVID-19 diagnosis from chest X-ray images. Appl Soft Comput 115:108250.&nbsp;<a href=\"https://doi.org/10.1016/j.asoc.2021.108250\">https://doi.org/10.1016/j.asoc.2021.108250</a></p>\n\n<p>G&uuml;lmez B (2022) Demand forecasting and production planning in a factory with time series analysis. In: International Research in Engineering Sciences. Egitim Publishing, Konya, pp 57&ndash;74</p>\n\n<p>G&uuml;lmez B (2022) MonkeypoxHybridNet: A hybrid deep convolutional neural network model for monkeypox disease detection. In: International Research in Engineering Sciences. Egitim Publishing, Konya, pp 49&ndash;64</p>\n\n<p>G&uuml;lmez B (2023) A novel deep neural network model based Xception and genetic algorithm for detection of COVID-19 from X-ray images. Ann Oper Res 328:617&ndash;641.&nbsp;<a href=\"https://doi.org/10.1007/s10479-022-05151-y\">https://doi.org/10.1007/s10479-022-05151-y</a></p>\n\n<p>G&uuml;lmez B (2023) Optimizing and comparison of market chain product distribution problem with different genetic algorithm versions. Osmaniye Korkut Ata Univ J Inst Sci Technol 6:180&ndash;196.&nbsp;<a href=\"https://doi.org/10.47495/okufbed.1117220\">https://doi.org/10.47495/okufbed.1117220</a></p>\n\n<p>G&uuml;lmez B (2023) Stock price prediction with optimized deep LSTM network with artificial rabbits optimization algorithm. Expert Syst Appl 227:120346.&nbsp;<a href=\"https://doi.org/10.1016/j.eswa.2023.120346\">https://doi.org/10.1016/j.eswa.2023.120346</a></p>\n\n<p>G&uuml;lmez B (2023) A novel deep learning model with the Grey Wolf Optimization algorithm for cotton disease detection. J Univers Comput Sci 6:595&ndash;626.&nbsp;<a href=\"https://doi.org/10.3897/jucs.94183\">https://doi.org/10.3897/jucs.94183</a></p>\n\n<p>G&uuml;lmez B, Kulluk S (2019) Social spider algorithm for training artificial neural networks. International J Business Anal 6:32&ndash;49.&nbsp;<a href=\"https://doi.org/10.4018/IJBAN.2019100103\">https://doi.org/10.4018/IJBAN.2019100103</a></p>\n\n<p>G&uuml;lmez B, Kulluk S (2023) Analysis and price prediction of secondhand vehicles in T&uuml;rkiye with big data and machine learning techniques. J Faculty Eng Arch Gazi Univ 38:2279&ndash;2290.&nbsp;<a href=\"https://doi.org/10.17341/gazimmfd.980840\">https://doi.org/10.17341/gazimmfd.980840</a></p>\n\n<p>G&uuml;lmez B (2024) Advancements in rice disease detection through convolutional neural networks: A comprehensive review. Heliyon 10:e33328.&nbsp;<a href=\"https://doi.org/10.1016/j.heliyon.2024.e33328\">https://doi.org/10.1016/j.heliyon.2024.e33328</a></p>\n\n<p>G&uuml;lmez B, Emmerich M, Fan Y (2024) Multi-objective optimization for green delivery routing problems with flexible time windows. Appl Artif Intell 38:2325302.&nbsp;<a href=\"https://doi.org/10.1080/08839514.2024.2325302\">https://doi.org/10.1080/08839514.2024.2325302</a></p>\n\n<p>Gunantara N (2018) A review of multi-objective optimization: methods and its applications. Cogent Eng 5:1502242.&nbsp;<a href=\"https://doi.org/10.1080/23311916.2018.1502242\">https://doi.org/10.1080/23311916.2018.1502242</a></p>\n\n<p>Gupta V, Vasudev M, Doegar A, Sambyal N (2021) Breast cancer detection from histopathology images using modified residual neural networks. Biocybernetics Biomedical Eng 41:1272&ndash;1287.&nbsp;<a href=\"https://doi.org/10.1016/j.bbe.2021.08.011\">https://doi.org/10.1016/j.bbe.2021.08.011</a></p>\n\n<p>Hajiakhondi-Meybodi Z, Mohammadi A, Abouei J (2021) Deep reinforcement learning for trustworthy and time-varying connection scheduling in a coupled UAV-based femtocaching architecture. IEEE Access 9:32263&ndash;32281.&nbsp;<a href=\"https://doi.org/10.1109/ACCESS.2021.3060323\">https://doi.org/10.1109/ACCESS.2021.3060323</a></p>\n\n<p>Igel C (2005) Multi-objective model selection for support Vector machines. In: Coello Coello CA, Hern&aacute;ndez Aguirre A, Zitzler E (eds) Evolutionary Multi-criterion optimization. Springer, Berlin, Heidelberg, pp 534&ndash;546</p>\n\n<p>Kim M (2021) Prediction of COVID-19 confirmed cases after vaccination: based on statistical and deep learning models. SciMedicine J 3:153&ndash;165.&nbsp;<a href=\"https://doi.org/10.28991/SciMedJ-2021-0302-7\">https://doi.org/10.28991/SciMedJ-2021-0302-7</a></p>\n\n<p>Kiziloluk S, Sert E (2022) COVID-CCD-Net: COVID-19 and colon cancer diagnosis system with optimized CNN hyperparameters using gradient-based optimizer. Med Biol Eng Comput 60:1595&ndash;1612.&nbsp;<a href=\"https://doi.org/10.1007/s11517-022-02553-9\">https://doi.org/10.1007/s11517-022-02553-9</a></p>\n\n<p>Lakhmiri D, Digabel SL, Tribes C (2021) HyperNOMAD: hyperparameter optimization of deep neural networks using mesh adaptive direct search. ACM Trans Math Softw 47:27:1&ndash;2727.&nbsp;<a href=\"https://doi.org/10.1145/3450975\">https://doi.org/10.1145/3450975</a></p>\n\n<p>Liang J, Xu W, Yue C et al (2019) Multimodal multiobjective optimization with differential evolution. Swarm Evol Comput 44:1028&ndash;1059.&nbsp;<a href=\"https://doi.org/10.1016/j.swevo.2018.10.016\">https://doi.org/10.1016/j.swevo.2018.10.016</a></p>\n\n<p>Lim YT, Cheng P-C, Rohatgi P, Clark JA (2009) Dynamic security policy learning. In: Proceedings of the first ACM workshop on Information security governance. Association for Computing Machinery, New York, NY, USA, pp 39&ndash;48</p>\n\n<p>Liu X, Tian J, Duan P et al (2024) GrMoNAS: a granularity-based multi-objective NAS framework for efficient medical diagnosis. Comput Biol Med 171.&nbsp;<a href=\"https://doi.org/10.1016/j.compbiomed.2024.108118\">https://doi.org/10.1016/j.compbiomed.2024.108118</a></p>\n\n<p>Mohammedqasem R, Mohammedqasim H, Asad Ali Biabani S et al (2023) Multi-objective deep learning framework for COVID-19 dataset problems. J King Saud Univ Sci 35.&nbsp;<a href=\"https://doi.org/10.1016/j.jksus.2022.102527\">https://doi.org/10.1016/j.jksus.2022.102527</a></p>\n\n<p>Muthumayil K, Buvana M, Sekar KR et al (2021) Optimized convolutional neural network for automatic detection of COVID-19. Comput Mater Continua 70:1159&ndash;1175.&nbsp;<a href=\"https://doi.org/10.32604/cmc.2022.017178\">https://doi.org/10.32604/cmc.2022.017178</a></p>\n\n<p>Mutlu MM (2021) Multi-objective and multi-modal network design model for a competitive public transportation system. Ege University</p>\n\n<p>Orta&ccedil;ay Z (2020) Multi-objective optimization based reallocation in land consolidation. Konya Technical University</p>\n\n<p>Osman AA, Al Daajani MM, Alsahafi AJ (2020) Re-positive coronavirus disease 2019 PCR test: could it be a reinfection? New Microbes New Infections 37:100748.&nbsp;<a href=\"https://doi.org/10.1016/j.nmni.2020.100748\">https://doi.org/10.1016/j.nmni.2020.100748</a></p>\n\n<p>Rahman CM, Rashid TA, Ahmed AM, Mirjalili S (2022) Multi-objective learner performance-based behavior algorithm with five multi-objective real-world engineering problems. Neural Comput Applic 34:6307&ndash;6329.&nbsp;<a href=\"https://doi.org/10.1007/s00521-021-06811-z\">https://doi.org/10.1007/s00521-021-06811-z</a></p>\n\n<p>Raikote, P. Covid-19 Image Dataset,&nbsp;<a href=\"https://www.kaggle.com/datasets/pranavraikokte/covid19-image-dataset\">https://www.kaggle.com/datasets/pranavraikokte/covid19-image-dataset</a>&nbsp;(2020).</p>\n\n<p>Rajagopal R, Karthick R, Meenalochini P, Kalaichelvi T (2023) Deep Convolutional spiking neural network optimized with arithmetic optimization algorithm for lung disease detection using chest X-ray images. Biomed Signal Process Control 79.&nbsp;<a href=\"https://doi.org/10.1016/j.bspc.2022.104197\">https://doi.org/10.1016/j.bspc.2022.104197</a></p>\n\n<p>Sar\u0131g&uuml;l M, Ozyildirim BM, Avci M (2019) Differential convolutional neural network. Neural Netw 116:279&ndash;287.&nbsp;<a href=\"https://doi.org/10.1016/j.neunet.2019.04.025\">https://doi.org/10.1016/j.neunet.2019.04.025</a></p>\n\n<p>Sayed GI (2022) A Novel Multi-objective Rat Swarm optimizer-based convolutional neural networks for the diagnosis of COVID-19 Disease. Autom Control Comput Sci 56:198&ndash;208.&nbsp;<a href=\"https://doi.org/10.3103/S0146411622030075\">https://doi.org/10.3103/S0146411622030075</a></p>\n\n<p>Shukla PK, Sandhu JK, Ahirwar A et al (2021) Multiobjective Genetic Algorithm and convolutional neural network based COVID-19 identification in chest X-Ray images.&nbsp;<a href=\"https://doi.org/10.1155/2021/7804540\">https://doi.org/10.1155/2021/7804540</a>. Math Probl Eng 2021:</p>\n\n<p>Singh D, Kumar V, Yadav V, Kaur M (2021) Deep neural network-based screening model for COVID-19-Infected patients using chest X-Ray images. Int J Pattern Recognit Artif Intell 35.&nbsp;<a href=\"https://doi.org/10.1142/S0218001421510046\">https://doi.org/10.1142/S0218001421510046</a></p>\n\n<p>Singha AK, Pathak N, Sharma N et al (2022) An Experimental Approach to diagnose Covid-19 using optimized CNN. Intell Autom Soft Comp 34:1065&ndash;1080.&nbsp;<a href=\"https://doi.org/10.32604/iasc.2022.024172\">https://doi.org/10.32604/iasc.2022.024172</a></p>\n\n<p>Song X, Wu N, Song S et al (2023a) Bipartite synchronization for cooperative-competitive neural networks with reaction&ndash;diffusion terms via dual event-triggered mechanism. Neurocomputing 550:126498.&nbsp;<a href=\"https://doi.org/10.1016/j.neucom.2023.126498\">https://doi.org/10.1016/j.neucom.2023.126498</a></p>\n\n<p>Song X, Wu N, Song S, Stojanovic V (2023b) Switching-like event-triggered state estimation for reaction&ndash;diffusion neural networks against DoS attacks. Neural Process Lett 55:8997&ndash;9018.&nbsp;<a href=\"https://doi.org/10.1007/s11063-023-11189-1\">https://doi.org/10.1007/s11063-023-11189-1</a></p>\n\n<p>Xu Y, Du J, Dai L-R, Lee C-H (2014) An experimental study on Speech Enhancement based on deep neural networks. IEEE Signal Process Lett 21:65&ndash;68.&nbsp;<a href=\"https://doi.org/10.1109/LSP.2013.2291240\">https://doi.org/10.1109/LSP.2013.2291240</a></p>\n\n<p>Yani M, Si SMTBI, MTCS ST (2019) Application of transfer learning using Convolutional Neural Network Method for Early Detection of Terry&rsquo;s nail. J Phys: Conf Ser 1201:012052.&nbsp;<a href=\"https://doi.org/10.1088/1742-6596/1201/1/012052\">https://doi.org/10.1088/1742-6596/1201/1/012052</a></p>\n\n<p>Zhang Q, Li H (2007) MOEA/D: a Multiobjective Evolutionary Algorithm based on decomposition. IEEE Trans Evol Comput 11:712&ndash;731.&nbsp;<a href=\"https://doi.org/10.1109/TEVC.2007.892759\">https://doi.org/10.1109/TEVC.2007.892759</a></p>\n\n<p>Zhuang Z, Tao H, Chen Y et al (2023) An optimal iterative Learning Control Approach for Linear Systems with Nonuniform Trial Lengths under Input constraints. IEEE Trans Syst Man Cybernetics: Syst 53:3461&ndash;3473.&nbsp;<a href=\"https://doi.org/10.1109/TSMC.2022.3225381\">https://doi.org/10.1109/TSMC.2022.3225381</a></p>\n\n<p>&nbsp;</p>", 
  "headline": "A new multi-objective hyperparameter optimization algorithm for COVID-19 detection from x-ray images", 
  "identifier": 273782, 
  "image": "https://aperta.ulakbim.gov.tr/static/img/logo/aperta_logo_with_icon.svg", 
  "license": "http://www.opendefinition.org/licenses/cc-by-sa", 
  "name": "A new multi-objective hyperparameter optimization algorithm for COVID-19 detection from x-ray images", 
  "url": "https://aperta.ulakbim.gov.tr/record/273782"
}