Skip to main content

Advertisement

SpringerLink
Log in

Two-stage deep learning model for Alzheimer’s disease detection and prediction of the mild cognitive impairment time

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Alzheimer’s disease (AD) is an irreversible neurodegenerative disease characterized by thinking, behavioral and memory impairments. Early prediction of conversion from mild cognitive impairment (MCI) to AD is still a challenging task. No study has been able to predict the exact conversion time of MCI patients. In addition, most studies have achieved poor performance making this prediction using only a small number of features (e.g., using only MRI images). Therefore, previous approaches have not gained the trust of medical experts. This study proposes a novel two-stage deep learning AD progression detection framework based on information fusion of several patient longitudinal multivariate modalities, including neuroimaging data, cognitive scores, cerebrospinal fluid biomarkers, neuropsychological battery markers, and demographics. The first stage of the progression detection framework employs a multiclass classification task that predicts a patient’s diagnosis (i.e., cognitively normal, MCI, or AD). In the second stage, a regression task that predicts the exact conversion time of MCI patients is used. The study is based on data of 1,371 subjects collected by the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Comprehensive experiments were carried out to evaluate the framework stages and find the optimal model for each stage. Proposed model was compared with various machine learning models, including decision tree (DT), random forest (RF), support vector machine (SVM), logistic regression (LR), and K-nearest neighbor (KNN). In the classification stage, the proposed long-short term memory (LSTM) model achieved an accuracy of 93.87%, precision of 94.070%, recall of 94.07%, and F1-score of 94.07%. The results showed that the LSTM model outperformed other machine learning models (i.e., decision tree by 2.48%, random forest by 1.27%, support vector machine by 1.86%, logistic regression by 1.59%, and K-nearest neighbor by 14.77%). In the regression stage, the proposed LSTM model achieved the best results (i.e., mean absolute error of 0.1375). Compared to other regular regressors, this LSTM model achieved less errors (i.e., 0.0064, 0.0152, 0.0338, 0.0118, 0.0198, and 0.0066, compared to DT, RF, SVM, LR, and KNN, respectively). By learning deep representation from patient high-dimensional longitudinal time-series data, the proposed LSTM model was more stable and medically acceptable. The framework may have a clinical impact as a predictive tool for AD progression detection due to its accurate results to predict the exact conversion time of MCI cases using patient time-series multimodalities data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Ritter K, Schumacher J, Weygandt M, Buchert R, Allefeld C, Haynes J-D (2015) Multimodal prediction of conversion to Alzheimer’s disease based on incomplete biomarkers. Alzheimers Dement (Amst) 1(2):206–215

    Article  Google Scholar 

  2. Zhang R, Simon G, Yu F (2017) Advancing Alzheimer’s research: a review of big data promises. Int J med inform 106:48–56

    Article  Google Scholar 

  3. Alzheimer Disease International: World Alzheimer Report 2018 (2018). https://www.alz.co.uk/research/world-report-2018 Accessed 2021

  4. Hong X, Lin R, Yang C, Cai C, Clawson K (2020) ADPM: an Alzheimer’s disease prediction model for time series neuroimage analysis. IEEE Access 8:62601–62609

    Article  Google Scholar 

  5. Iddi S, Li D, Aisen PS, Rafii MS, Thompson WK, Donohue MC (2019) Predicting the course of Alzheimer’s progression. Brain Inform 6(1):1–18

    Article  Google Scholar 

  6. Lu S, Xia Y, Cai W, Fulham M, Feng DD (2017) Alzheimer’s sisease neuroimaging initiative: early identification of mild cognitive impairment using incomplete random forest-robust support vector machine and FDG-PET imaging. Comput Med Imaging Graph 60:35–41

    Article  Google Scholar 

  7. Zhou T, Thung K-H, Liu M, Shi F, Zhang C, Shen D (2020) Multi-modal latent space inducing ensemble SVM classifier for early dementia diagnosis with neuroimaging data. Med image anal 60:101630

    Article  Google Scholar 

  8. Moscoso A, Silva-Rodríguez J, Aldrey JM, Cortés J, Fernández-Ferreiro A, Gómez-Lado N, Ruibal Á, Aguiar P (2019) Alzheimer’s disease neuroimaging initiative: prediction of Alzheimer’s disease dementia with MRI beyond the short-term: implications for the design of predictive models. Neuroimage Clin 23:101837

    Article  Google Scholar 

  9. Zhang D, Wang Y, Zhou L, Yuan H, Shen D (2011) Alzheimer’s disease neuroimaging initiative: multimodal classification of Alzheimer’s disease and mild cognitive impairment. Neuroimage 55(3):856–867

    Article  Google Scholar 

  10. Hong X, Lin R, Yang C, Zeng N, Cai C, Gou J, Yang J (2019) Predicting Alzheimer’s disease using LSTM. IEEE Access 7:80893–80901

    Article  Google Scholar 

  11. Filipovych R, Davatzikos C (2010) Alzheimer’s disease neuroimaging initiative: semi-supervised pattern classification of medical images: application to mild cognitive impairment (MCI). Neuroimage 55(3):1109–1119

    Article  Google Scholar 

  12. Moore PJ, Lyons TJ, Gallacher J (2019) Alzheimer’s disease neuroimaging initiative: random forest prediction of Alzheimer’s disease using pairwise selection from time series data. PLoS One 14(2):0211558

    Article  Google Scholar 

  13. Rallabandi VPS, Tulpule K, Gattu M (2020) Automatic classification of cognitively normal, mild cognitive impairment and Alzheimer’s disease using structural MRI analysis. Inform Med Unlocked 18:100305

    Article  Google Scholar 

  14. Jin M, Deng W (2018) Predication of different stages of Alzheimer’s disease using neighborhood component analysis and ensemble decision tree. J Neurosci Meth 302:35–41

    Article  Google Scholar 

  15. Klomp A, Caan MW, Denys D, Nederveen AJ, Reneman L (2012) Feasibility of ASL-based phMRI with a single dose of oral citalopram for repeated assessment of serotonin function. Neuroimage 63(3):1695–1700

    Article  Google Scholar 

  16. Yau W-YW, Tudorascu DL, McDade EM, Ikonomovic S, James JA, Minhas D, Mowrey W, Sheu LK, Snitz BE, Weissfeld L et al (2015) Longitudinal assessment of neuroimaging and clinical markers in autosomal dominant Alzheimer’s disease: a prospective cohort study. The Lancet Neurol. 14(8):804–813

    Article  Google Scholar 

  17. Martí-Juan G, Sanroma-Guell G, Piella G (2020) A survey on machine and statistical learning for longitudinal analysis of neuroimaging data in Alzheimer’s disease. Comp Meth Programs in Biomed 189:105348

    Article  Google Scholar 

  18. Forouzannezhad P, Abbaspour A, Fang C, Cabrerizo M, Loewenstein D, Duara R, Adjouadi M (2019) A survey on applications and analysis methods of functional magnetic resonance imaging for Alzheimer’s disease. J Neurosci Meth 317:121–140

    Article  Google Scholar 

  19. Liu L, Zhao S, Chen H, Wang A (2020) A new machine learning method for identifying Alzheimer’s disease. Simul Modell Pract Theory 99:102023

    Article  Google Scholar 

  20. Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert M-O, Chupin M, Benali H, Colliot O (2010) Alzheimer’s disease neuroimaging initiative: automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage 56(2):766–781

    Article  Google Scholar 

  21. Wang T, Qiu RG, Yu M (2018) Predictive modeling of the progression of Alzheimer’s disease with recurrent neural networks. Scientific Rep 8(1):1–12

    Google Scholar 

  22. Wang X, Qi J, Yang Y, Yang P (2019) A survey of disease progression modeling techniques for alzheimer’s diseases. In: 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), vol. 1, pp. 1237–1242. IEEE

  23. El-Sappagh S, Alonso JM, Islam SR, Sultan AM, Kwak KS (2021) A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease. Scientific Rep 11(1):1–26

    Google Scholar 

  24. El-Sappagh S, Abuhmed T, Islam SR, Kwak KS (2020) Multimodal multitask deep learning model for Alzheimer’s disease progression detection based on time series data. Neurocomputing 412:197–215

    Article  Google Scholar 

  25. El-Sappagh S, Saleh H, Sahal R, Abuhmed T, Islam SR, Ali F, Amer E (2021) Alzheimer’s disease progression detection model based on an early fusion of cost-effective multimodal data. Fut Gener Comp Syst 115:680–699

    Article  Google Scholar 

  26. Zhang D, Shen D (2011) Alzheimer’s disease neuroimaging initiative: multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease. Neuroimage 59(2):895–907

    Article  MathSciNet  Google Scholar 

  27. Tabarestani S, Aghili M, Eslami M, Cabrerizo M, Barreto A, Rishe N, Curiel RE, Loewenstein D, Duara R, Adjouadi M (2020) A distributed multitask multimodal approach for the prediction of Alzheimer’s disease in a longitudinal study. NeuroImage 206:116317

    Article  Google Scholar 

  28. Alberdi A, Aztiria A, Basarab A (2016) On the early diagnosis of Alzheimer’s Disease from multimodal signals: a survey. Artificial intelligence in medicine 71:1–29

    Article  Google Scholar 

  29. Moradi E, Pepe A, Gaser C, Huttunen H, Tohka J (2014) Alzheimer’s disease neuroimaging initiative: machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects. Neuroimage 104:398–412

    Article  Google Scholar 

  30. Pillai PS, Leong T-Y (2015) Alzheimer’s disease neuroimaging initiative: fusing heterogeneous data for Alzheimer’s disease classification. Stud Health Technol Inform 216:731–735

    Google Scholar 

  31. Ewers M, Walsh C, Trojanowski JQ, Shaw LM, Petersen RC, Jack CR Jr, Feldman HH, Bokde AL, Alexander GE, Scheltens P et al (2012) Prediction of conversion from mild cognitive impairment to Alzheimer’s disease dementia based upon biomarkers and neuropsychological test performance. Neurobiol Aging 33(7):1203–1214

    Article  Google Scholar 

  32. Liu W, Zhang B, Zhang Z, Zhou X-H (2013) Joint modeling of transitional patterns of Alzheimer’s disease. PloS One 8(9):75487

    Article  Google Scholar 

  33. Huang L, Gao Y, Jin Y, Thung K-H, Shen D (2015) Soft-split sparse regression based random forest for predicting future clinical scores of Alzheimer’s disease. In: International Workshop on Machine Learning in Medical Imaging, pp. 246–254. Springer

  34. Lee G, Nho K, Kang B, Sohn K-A, Kim D (2019) Predicting Alzheimer’s disease progression using multi-modal deep learning approach. Scientific Rep 9(1):1–12

    Google Scholar 

  35. Li H, Habes M, Wolk D, Fan Y (2019) A deep learning model for early prediction of Alzheimer’s disease dementia based on hippocampal magnetic resonance imaging data. Alzheimer’s & Dementia Alzheimer’s dise neuroimag Init 15:1059–1070

    Google Scholar 

  36. Qiu S, Chang GH, Panagia M, Gopal DM, Au R, Kolachalama VB (2018) Fusion of deep learning models of MRI scans, Mini-Mental State Examination, and logical memory test enhances diagnosis of mild cognitive impairment. Alzheimer’s & Dementia: Diagn, Assessment & Dis Monit 10:737–749

    Google Scholar 

  37. Forouzannezhad P, Abbaspour A, Li C, Fang C, Williams U, Cabrerizo M, Barreto A, Andrian J, Rishe N, Curiel RE et al (2020) A Gaussian-based model for early detection of mild cognitive impairment using multimodal neuroimaging. J Neurosci Meth 333:108544

    Article  Google Scholar 

  38. Cheng B, Liu M, Zhang D, Munsell BC, Shen D (2015) Domain transfer learning for MCI conversion prediction. IEEE Trans Biomed Eng 62(7):1805–1817

    Article  Google Scholar 

  39. Wee C-Y, Yap P-T, Shen D (2012) Alzheimer’s disease neuroimaging initiative: prediction of Alzheimer’s disease and mild cognitive impairment using cortical morphological patterns. Hum Brain Mapp 34(12):3411–3425

    Article  Google Scholar 

  40. Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Cedarbaum J, Donohue MC, Green RC, Harvey D, Jack CR Jr et al (2015) Impact of the Alzheimer’s disease neuroimaging initiative, 2004 to 2014. Alzheimer’s & Dementia 11(7):865–884

    Article  Google Scholar 

  41. Liu F, Zhou L, Shen C, Yin J (2013) Multiple kernel learning in the primal for multimodal Alzheimer’s disease classification. IEEE J Biomed Health Inform 18(3):984–990

    Google Scholar 

  42. Cho Y, Seong J-K, Jeong Y, Shin SY (2011) Alzheimer’s disease neuroimaging initiative: individual subject classification for Alzheimer’s disease based on incremental learning using a spatial frequency representation of cortical thickness data. Neuroimage 59(3):2217–2230

    Article  Google Scholar 

  43. Suresh H, Hunt N, Johnson A, Celi LA, Szolovits P, Ghassemi M (2017) Clinical intervention prediction and understanding using deep networks. arXiv preprint arXiv:1705.08498

  44. Tian C, Ma J, Zhang C, Zhan P (2018) A deep neural network model for short-term load forecast based on long short-term memory network and convolutional neural network. Energies 11(12):3493

    Article  Google Scholar 

  45. Spasov S, Passamonti L, Duggento A, Liò P, Toschi N (2019) Alzheimer’s disease neuroimaging initiative: a parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer’s disease. Neuroimage 189:276–287

    Article  Google Scholar 

  46. Tabarestani S, Aghili M, Shojaie M, Freytes C, Cabrerizo M, Barreto A, Rishe N, Curiel RE, Loewenstein D, Duara R et al. (2019) Longitudinal prediction modeling of alzheimer disease using recurrent neural networks. In: 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), pp. 1–4. IEEE

  47. Choi H, Jin KH (2018) Alzheimer’s disease neuroimaging initiative: predicting cognitive decline with deep learning of brain metabolism and amyloid imaging. Behav Brain Res 344:103–109

    Article  Google Scholar 

  48. Liu M, Zhang J, Adeli E, Shen D (2018) Joint classification and regression via deep multi-task multi-channel learning for Alzheimer’s disease diagnosis. IEEE Trans Biomed Eng 66(5):1195–1206

    Article  Google Scholar 

  49. Gupta Y, Lama RK, Kwon G-R, Weiner MW, Aisen P, Weiner M, Petersen R, Jack CR Jr, Jagust W, Trojanowki JQ et al (2019) Prediction and classification of Alzheimer’s disease based on combined features from apolipoprotein-E genotype, cerebrospinal fluid, MR, and FDG-PET imaging biomarkers. Front Comp Neurosci 13:72

    Article  Google Scholar 

  50. Yao D, Calhoun VD, Fu Z, Du Y, Sui J (2018) An ensemble learning system for a 4-way classification of Alzheimer’s disease and mild cognitive impairment. J Neurosci Meth 302:75–81

    Article  Google Scholar 

  51. Bucholc M, Ding X, Wang H, Glass DH, Wang H, Prasad G, Maguire LP, Bjourson AJ, McClean PL, Todd S et al (2019) A practical computerized decision support system for predicting the severity of Alzheimer’s disease of an individual. Expert Syst Appl 130:157–171

    Article  Google Scholar 

  52. Nanni L, Lumini A, Zaffonato N (2018) Ensemble based on static classifier selection for automated diagnosis of mild cognitive impairment. J Neurosci Meth 302:42–46

    Article  Google Scholar 

  53. Desikan, R.: S egonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ, (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31:968–980

  54. MCKHANN G (1984) Report of the NINCDS-ADRDA work group under the auspices of department of health and human service task force on Alzheimer’s disease. Neurology 34, 939–944

  55. Quinlan J (1993) C4. 5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA

  56. Cutler A, Cutler DR, Stevens JR (2012) Random forests. In: Ensemble Machine Learning, pp. 157–175

  57. Andrew AM (2001) An introduction to support vector machines and other kernel-based learning methods. Kybernetes

  58. Barber D (2012) Bayesian reasoning and machine learning

  59. Smola A, Scholkopf B (2004) A tutorial on support vector regression. Stat Comp 14:199–222

    Article  MathSciNet  Google Scholar 

  60. Wright RE (1995) Logistic regression

  61. Hochreiter S (1997) JA1 4 rgen Schmidhuber.“Long Short-Term Memory”. Neural Computation 9(8)

  62. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artificial Intell Res 16:321–357

    Article  MATH  Google Scholar 

  63. Ledig C, Schuh A, Guerrero R, Heckemann RA, Rueckert D (2018) Structural brain imaging in Alzheimer’s disease and mild cognitive impairment: biomarker analysis and shared morphometry database. Scientific Rep 8(1):1–16

    Google Scholar 

  64. Klöppel S, Abdulkadir A, Jack CR Jr, Koutsouleris N, Mourão-Miranda J, Vemuri P (2012) Diagnostic neuroimaging across diseases. Neuroimage 61(2):457–463

    Article  Google Scholar 

  65. Klein-Koerkamp Y, Heckemann RA, Ramdeen KT, Moreaud O, Keignart S, Krainik A, Hammers A, Baciu M, Hot P (2014) Alzheimer’sdisease neuroimaging initiative: amygdalar atrophy in early Alzheimer’s disease. Curr Alzheimer Res 11(3):239–252

    Article  Google Scholar 

  66. Cui R, Liu M (2019) Alzheimer’s disease neuroimaging initiative: RNN-based longitudinal analysis for diagnosis of Alzheimer’s disease. Comput Med Imag Graphics 73:1–10

    Article  Google Scholar 

  67. Abuhmed T, El-Sappagh S, Alonso JM (2021) Robust hybrid deep learning models for Alzheimer’s progression detection. Knowl-Based Syst 213:106688

    Article  Google Scholar 

  68. Sorensen L, Nielsen M (2018) Alzheimer’s Disease Neuroimaging I. Ensemble support vector machine classification of dementia using structural MRI and mini-mental state examination. J Neurosci Methods 302, 66–74

  69. Dimitriadis SI, Liparas D, Tsolaki MN (2017) Alzheimer’s disease neuroimaging initiative: random forest feature selection, fusion and ensemble strategy: combining multiple morphological MRI measures to discriminate among healhy elderly, MCI, cMCI and alzheimer’s disease patients: from the alzheimer’s disease neuroimaging initiative (ADNI) database. J Neurosci Meth 302:14–23

    Article  Google Scholar 

  70. Maris E, Oostenveld R (2007) Nonparametric statistical testing of EEG-and MEG-data. J Neurosci Meth 164(1):177–190

    Article  Google Scholar 

  71. Ebadi A (2017) Dalboni da Rocha JL, Nagaraju DB, Tovar-Moll F., Bramati I., Coutinho G., et al. Ensemble classification of Alzheimer’s disease and mild cognitive impairment based on complex graph measures from diffusion tensor images. Front. Neurosci 11(56), 10–3389

  72. xplainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Inform Fus 58, 82–115 (2020)

  73. Mencar C, Alonso JM (2019) Paving the Way to Explainable Artificial Intelligence with Fuzzy Modeling. In: Fuller R, Giove S, Masulli F (eds) Fuzzy Logic and Applications. Springer, Cham, pp 215–227

  74. Alonso JM, Bugarin A (2019) ExpliClas: Automatic Generation of Explanations in Natural Language for Weka Classifiers. In: 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–6

  75. Keane MT, Kenny EM (2019) How case-based reasoning explains neural networks: A theoretical analysis of XAI using post-hoc explanation-by-example from a survey of ANN-CBR twin-systems. In: International Conference on Case-Based Reasoning, pp. 155–171. Springer

  76. Shoaip N, Rezk A, EL-Sappagh S, Abuhmed T, Barakat S, Elmogy M (2021) Alzheimer’s disease diagnosis based on a semantic rule-based modeling and reasoning approach. CMC-Computers Material & Continua 69(3), 3531–3548

Download references

Funding

This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ICT Creative Consilience Program (IITP-2021-2020-0-01821) supervised by the IITP (Institute for Information \& communications Technology Planning \& Evaluation), and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A2C1011198).

Author information

Authors and Affiliations

  1. Faculty of Computer Science and Engineering, Galala University, Suez, 435611, Egypt

    Shaker El-Sappagh

  2. Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha, 13518, Egypt

    Shaker El-Sappagh

  3. Faculty of Computers and Artificial Intelligence, South Valley University, Hurghada, Egypt

    Hager Saleh

  4. Department of Software, Sejong University, Seoul, South Korea

    Farman Ali

  5. Faculty of Computer Science, Misr International University, Cairo, Egypt

    Eslam Amer

  6. College of Computing and Informatics, Sungkyunkwan University, Suwon, South Korea

    Shaker El-Sappagh & Tamer Abuhmed

Authors
  1. Shaker El-Sappagh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hager Saleh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Farman Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Eslam Amer
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tamer Abuhmed
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Data collection, preparation, and analysis were performed by Shaker El-Sappagh, Hager Saleh, and Farman Ali. The first draft of the manuscript was written by Shaker El-Sappagh and Hager Saleh. Eslam Amer, Farman Ali, and Tamer ABUHMED contributed to methodology, and writing–review and editing. Tamer ABUHMED contributed to resources, supervision, funding acquisition and project administration. All authors read and approved the manuscript.

Corresponding author

Correspondence to Tamer Abuhmed.

Ethics declarations

Conflicts of interest

All authors declare that they have no conflicts of interest.

Human and animal rights

This research study was conducted retrospectively using human subject data made available by Alzheimer’s disease Neuroimaging Initiative (ADNI).

Reproducibility

For reproducibility purposes, readers can find the project code at this link: https://github.com/hagersalehahmed/Alzheimer. Because of data privacy, we cannot share the dataset, but a complete description of the used feature set and patient roster IDs are available on Github.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file 1 (pdf 400 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

El-Sappagh, S., Saleh, H., Ali, F. et al. Two-stage deep learning model for Alzheimer’s disease detection and prediction of the mild cognitive impairment time. Neural Comput & Applic 34, 14487–14509 (2022). https://doi.org/10.1007/s00521-022-07263-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-07263-9

Keywords

Search

Navigation