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LSAE: Autoencoder Latent Space for Dimensionality Reduction-Based Approach for COVID-19 Classification and Detection Task Using Chest X-ray

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Abstract

The novel coronavirus 2019 (COVID-19) has rapidly spread, evolving into a global epidemic. Existing pharmaceutical techniques and diagnostic tests, such as reverse transcription–polymerase chain reaction (RT-PCR) and serology tests, are time-consuming, expensive, and require well-equipped laboratories for analysis. This restricts their accessibility to a broader population. The need for a simple and accurate screening method is imperative to identify infected individuals and curtail the virus’s propagation. In this paper, we introduce a novel COVID-19 classification and detection approach (LSAE, latent space autoencoder) based on chest X-ray image scans. Initially, the high dimensionality of input data is compressed into a reduced representation (latent space), preserving crucial features while discarding noise. This latent space subsequently serves as the input to build an efficient SVM classifier for COVID-19 detection. Experimental outcomes using the COVID-19 dataset are promising as they confirm the rapidity and detection capability of the proposed LSAE.

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Data Availability

The datasets used during the current study are freely available in the UCI repository.

Code Availability

The code will be available upon request to reviewers.

References

  1. Cascella M, Rajnik M, Aleem A, Dulebohn SC, Di Napoli R (2022) Features, evaluation, and treatment of coronavirus (COVID-19). Statpearls [Internet]

  2. Pokhrel P, Hu C, Mao H (2020) Detecting the coronavirus (COVID-19). ACS Sens 5(8):2283–2296

  3. Sohrabi C, Mathew G, Franchi T, Kerwan A, Griffin M, Del Mundo JSC, Ali SA, Agha M, Agha R (2021) Impact of the coronavirus (COVID-19) pandemic on scientific research and implications for clinical academic training - a review. Int J Surg 86:57–63. https://doi.org/10.1016/j.ijsu.2020.12.008

    Article  PubMed  PubMed Central  Google Scholar 

  4. Worobey M (2021) Dissecting the early COVID-19 cases in Wuhan. Science 374(6572):1202–1204. https://doi.org/10.1126/science.abm4454

    Article  ADS  CAS  PubMed  Google Scholar 

  5. Jiang DH, Roy DJ, Gu BJ, Hassett LC, McCoy RG (2021) Postacute sequelae of severe acute respiratory syndrome coronavirus 2 infection: a state-of-the-art review. Basic Transl Sci 6(9–10):796–811. https://doi.org/10.1016/j.jacbts.2021.07.002

    Article  Google Scholar 

  6. Teymouri M, Mollazadeh S, Mortazavi H, Ghale-Noie ZN, Keyvani V, Aghababaei F, Hamblin MR, Abbaszadeh-Goudarzi G, Pourghadamyari H, Hashemian SMR et al (2021) Recent advances and challenges of RT-PCR tests for the diagnosis of COVID-19. Pathol Res Pract 221:153443. https://doi.org/10.1016/j.prp.2021.153443

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Giri B, Pandey S, Shrestha R, Pokharel K, Ligler FS, Neupane BB (2021) Review of analytical performance of COVID-19 detection methods. Anal Bioanal Chem 413(1):35–48

    Article  CAS  PubMed  Google Scholar 

  8. Aishwarya T, Ravi Kumar V (2021) Machine learning and deep learning approaches to analyze and detect COVID-19: a review. SN Comput Sci 2(3):226. https://doi.org/10.1007/s42979-021-00605-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Wang S, Kang B, Ma J, Zeng X, Xiao M, Guo J, Cai M, Yang J, Li Y, Meng X et al (2021) A deep learning algorithm using CT images to screen for corona virus disease (COVID-19). Eur Radiol 31:6096–6104

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Eskandarian R, Alizadehsani R, Behjati M, Zahmatkesh M, Sani ZA, Haddadi A, Kakhi K,Roshanzamir M, Shoeibi A, Hussain S et al (2023) Identification of clinical features associated with mortality in COVID-19 patients. In: Operations Research Forum, vol. 4. Springer. p 16. https://doi.org/10.1007/s43069-022-00191-3

  11. Alshazly H, Linse C, Abdalla M, Barth E, Martinetz T (2021) COVID-nets: deep CNN architectures for detecting COVID-19 using chest CT scans. Peer J Comput Sci 7:655

    Article  Google Scholar 

  12. Fang Y, Zhang H, Xie J, Lin M, Ying L, Pang P, Ji W (2020) Sensitivity of chest CT for COVID-19: comparison to RT-PCR. Radiology 296(2):115–117

    Article  Google Scholar 

  13. Dede G, Filiopoulou E, Paroni D-V, Michalakelis C, Kamalakis T (2023) Analysis and evaluation of major COVID-19 features: a pairwise comparison approach. In: Operations Research Forum, vol. 4. Springer. p 15. https://doi.org/10.1007/s43069-023-00201-y

  14. Miron R, Moisii C, Dinu S, Breaban M (2021) COVID detection in chest CTS: improving the baseline on COV19-CT-DB. Preprint at http://arxiv.org/abs/2107.04808, https://doi.org/10.48550/arXiv.2107.04808

  15. Thakur S, Kumar A (2021) X-ray and CT-scan-based automated detection and classification of COVID-19 using convolutional neural networks (CNN). Biomed Signal Process Control 69:102920. https://doi.org/10.1016/j.bspc.2021.102920

    Article  PubMed  PubMed Central  Google Scholar 

  16. Ahmed W, Simpson SL, Bertsch PM, Bibby K, Bivins A, Blackall LL, Bofill-Mas S, Bosch A, Brandão J, Choi PM et al (2022) Minimizing errors in RT-PCR detection and quantification of SARS-Cov-2 RNA for wastewater surveillance. Sci Total Environ 805:149877. https://doi.org/10.1016/j.scitotenv.2021.149877

    Article  ADS  CAS  PubMed  Google Scholar 

  17. Hemdan EE-D, Shouman MA, Karar ME (2020) COVIDX-Net: a framework of deep learning classifiers to diagnose COVID-19 in X-ray images. Preprint at http://arxiv.org/abs/2003.11055, https://doi.org/10.48550/arXiv.2003.11055

  18. Salehi S, Abedi A, Balakrishnan S, Gholamrezanezhad A et al (2020) Coronavirus disease 2019 (COVID-19): a systematic review of imaging findings in 919 patients. Ajr Am J Roentgenol 215(1):87–93. https://doi.org/10.2214/ajr.20.23034

    Article  PubMed  Google Scholar 

  19. Parekh M, Donuru A, Balasubramanya R, Kapur S (2020) Review of the chest CT differential diagnosis of ground-glass opacities in the COVID era. Radiology 297(3):289–302. https://doi.org/10.1148/radiol.2020202504

    Article  Google Scholar 

  20. Shamrat FJM, Azam S, Karim A, Ahmed K, Bui FM, De Boer F (2023) High-precision multiclass classification of lung disease through customized MobileNetV2 from chest X-ray images. Comput Biol Med 155:106646. https://doi.org/10.1016/j.compbiomed.2023.106646

    Article  PubMed  Google Scholar 

  21. Shamrat FJM, Azam S, Karim A, Islam R, Tasnim Z, Ghosh P, De Boer F (2022) LungNet22: a fine-tuned model for multiclass classification and prediction of lung disease using X-ray images. J Personal Med 12(5):680. https://doi.org/10.3390/jpm12050680

    Article  Google Scholar 

  22. Shamrat F, Chakraborty S, Ahammad R, Shitab TM, Kazi MA, Hossain A, Mahmud I (2022) Analysing most efficient deep learning model to detect COVID-19 from computer tomography images. Indones J Electr Eng Comput Sci 26(1):462–471. https://doi.org/10.11591/ijeecs.v26.i1.pp462-471

    Article  Google Scholar 

  23. Peek N, Combi C, Marin R, Bellazzi R (2015) Thirty years of artificial intelligence in medicine (AIME) conferences: a review of research themes. Artif Intell Med 65(1):61–73. https://doi.org/10.1016/j.artmed.2015.07.003

    Article  PubMed  Google Scholar 

  24. Becker A (2019) Artificial intelligence in medicine: what is it doing for us today? Health Pol Technol 8(2):198–205. https://doi.org/10.1016/j.hlpt.2019.03.004

    Article  Google Scholar 

  25. Noorbakhsh-Sabet N, Zand R, Zhang Y, Abedi V (2019) Artificial intelligence transforms the future of health care. Am J Med 132(7):795–801

    Article  PubMed  PubMed Central  Google Scholar 

  26. Akter S, Shamrat FJM, Chakraborty S, Karim A, Azam S (2021) COVID-19 detection using deep learning algorithm on chest X-ray images. Biology 10(11):1174. https://doi.org/10.3390/biology10111174

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Pisner DA, Schnyer DM (2020) Support vector machine. In: Machine Learning. Elsevier. pp 101–121

  28. Shamrat FJM, Akter S, Azam S, Karim A, Ghosh P, Tasnim Z, Hasib KM, De Boer F, Ahmed K (2023) AlzheimerNet: an effective deep learning based proposition for Alzheimer’s disease stages classification from functional brain changes in magnetic resonance images. IEEE Access 11:16376–16395. https://doi.org/10.1109/ACCESS.2023.3244952

    Article  Google Scholar 

  29. Akhiat Y, Asnaoui Y, Chahhou M, Zinedine A (2021) A new graph feature selection approach. In: 2020 6th IEEE Congress on Information Science and Technology (CiSt). IEEE. pp 156–161

  30. Akhiat Y, Manzali Y, Chahhou M, Zinedine A (2021) A new noisy random forest-based method for feature selection. Cybern Inf Technol 21(2):10–28

    Google Scholar 

  31. Akhiat Y, Chahhou M, Zinedine A (2019) Ensemble feature selection algorithm. Int J Intell Syst Appl 11(1):24

    Google Scholar 

  32. Akhiat Y, Chahhou M, Zinedine A (2018) Feature selection based on graph representation. In: 2018 IEEE 5th International Congress on Information Science and Technology (CiSt). IEEE. pp 232–237

  33. Bouchlaghem Y, Akhiat Y, Amjad S (2022) Feature selection: a review and comparative study. In: E3S Web of Conferences, vol. 351. EDP Sciences. p 01046

  34. Manzali Y, Akhiat Y, Chahhou M, Elmohajir M, Zinedine A (2022) Reducing the number of trees in a forest using noisy features. Evol Syst 1–18

  35. Akhiat Y, Touchanti K, Zinedine A, Chahhou M (2023) IDS-EFS: ensemble feature selection-based method for intrusion detection system. Multimed Tools Appl 1–21. https://doi.org/10.1007/s11042-023-15977-8

  36. Ansari G, Ahmad T, Doja MN (2019) Hybrid filter-wrapper feature selection method for sentiment classification. Arab J Sci Eng 44:9191–9208. https://doi.org/10.1007/s13369-019-04064-6

    Article  Google Scholar 

  37. Dabas N, Ahlawat P, Sharma P (2022) An effective malware detection method using hybrid feature selection and machine learning algorithms. Arab J Sci Eng 1–19. https://doi.org/10.1007/s13369-022-07309-z

  38. Hasija S, Akash P, Hemanth MB, Kumar A, Sharma S (2022) A novel approach for detection of COVID-19 and pneumonia using only binary classification from chest CT-scans. Neurosci Inform 2(4):100069. https://doi.org/10.1016/j.neuri.2022.100069

    Article  PubMed  PubMed Central  Google Scholar 

  39. Al-Khafagy AM, Hashim SR, Enad RA (2022) A unique deep-learning-based model with chest X-ray image for diagnosing COVID-19. Indones J Electr Eng Comput Sci 28(2):1147–1154. https://doi.org/10.11591/ijeecs.v28.i2.pp1147-1154

    Article  Google Scholar 

  40. Panwar H, Gupta P, Siddiqui MK, Morales-Menendez R, Singh V (2020) Application of deep learning for fast detection of COVID-19 in X-rays using nCOVnet. Chaos Solit Fract 138:109944. https://doi.org/10.1016/j.chaos.2020.109944

    Article  MathSciNet  Google Scholar 

  41. Naeem H, Bin-Salem AA (2021) A CNN-LSTM network with multi-level feature extraction-based approach for automated detection of coronavirus from CT scan and X-ray images. Appl Soft Comput 113:107918. https://doi.org/10.1016/j.asoc.2021.107918

    Article  PubMed  PubMed Central  Google Scholar 

  42. Hemdan EE-D, Shouman MA, Karar ME (2020) COVIDX-Net: a framework of deep learning classifiers to diagnose COVID-19 in X-ray images. Preprint at http://arxiv.org/abs/2003.11055. https://doi.org/10.48550/arXiv.2003.11055

  43. Das NN, Kumar N, Kaur M, Kumar V, Singh D (2022) Automated deep transfer learning-based approach for detection of COVID-19 infection in chest X-rays. IRBM 43(2):114–119. https://doi.org/10.1016/j.irbm.2020.07.001

    Article  Google Scholar 

  44. Xie X, Zhong Z, Zhao W, Zheng C, Wang F, Liu J (2020) Chest CT for typical coronavirus disease 2019 (COVID-19) pneumonia: relationship to negative RT-PCR testing. Radiology 296(2):41–45. https://doi.org/10.1148/radiol.2020200343

    Article  Google Scholar 

  45. Huang P, Liu T, Huang L, Liu H, Lei M, Xu W, Hu X, Chen J, Liu B (2020) Use of chest CT in combination with negative RT-PCR assay for the 2019 novel coronavirus but high clinical suspicion. Radiology 295(1):22–23. https://doi.org/10.1148/radiol.2020200330

    Article  PubMed  Google Scholar 

  46. Cervantes J, Garcia-Lamont F, Rodríguez-Mazahua L, Lopez A (2020) A comprehensive survey on support vector machine classification: applications, challenges and trends. Neurocomputing 408:189–215

  47. Sheykhmousa M, Mahdianpari M, Ghanbari H, Mohammadimanesh F, Ghamisi P, Homayouni S (2020) Support vector machine versus random forest for remote sensing image classification: a meta-analysis and systematic review. IEEE J Select Topics Appl Earth Observ Remote Sens 13:6308–6325

    Article  ADS  Google Scholar 

  48. Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, Liu T, Wang X, Wang G, Cai J et al (2018) Recent advances in convolutional neural networks. Patt Recognit 77:354–377. https://doi.org/10.1016/j.patcog.2017.10.013

    Article  ADS  Google Scholar 

  49. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. Preprint at http://arxiv.org/abs/1409.1556. https://doi.org/10.48550/arXiv.1409.1556

  50. Rodríguez-Fdez I, Canosa A, Mucientes M, Bugarín A (2015) STAC: a web platform for the comparison of algorithms using statistical tests. In: Proceedings of the 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Author notes

  1. Yassine Akhiat, Kaouthar Touchanti, and Souad Amjad contributed equally to this work.

Authors and Affiliations

  1. TIMS, FS, Abdelmalek Essaadi University, Tetouan, Morocco

    Younes Bouchlaghem & Souad Amjad

  2. Departement of informatics, Faculty of Sciences, USMBA, Fez, Morocco

    Yassine Akhiat & Kaouthar Touchanti

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  1. Younes Bouchlaghem
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  2. Yassine Akhiat
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  3. Kaouthar Touchanti
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  4. Souad Amjad
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YB and YA wrote the main manuscript. KT proposed the methodology and prepared the figures. All authors reviewed the manuscript.

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Correspondence to Younes Bouchlaghem.

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Bouchlaghem, Y., Akhiat, Y., Touchanti, K. et al. LSAE: Autoencoder Latent Space for Dimensionality Reduction-Based Approach for COVID-19 Classification and Detection Task Using Chest X-ray. Oper. Res. Forum 4, 95 (2023). https://doi.org/10.1007/s43069-023-00278-5

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