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Transfer Learning Fusion and Stacked Auto-encoders for Viral Lung Disease Classification

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Abstract

The objective of this research endeavor is to identify an effective model for the classification of multiple viral respiratory diseases, encompassing COVID-19. The feature extraction phase from medical images constitutes a formidable challenge in achieving optimal disease classification outcomes. In this work, a selection of the best models among several popular transfer learning (TL) models is realized. The concatenation of the best models for better features extraction is used; the deep learning (DL) methods for deep features extraction and deep data reduction were applied for an optimal classification. This paper includes two studies, the first was applied to binary classification (COVID-19/Normal) and the second is concerned with multi-classification (COVID-19/Normal/VPneumonia). The validation of the proposed approaches is made on a big datasets: (i) binary classification 4800 COVID-19 and 4803 Normal images for the two cases Chest X-Ray (CXR) and Computed Tomography (CT) scans, and (ii) multi-class classification 3931 COVID-19, 3931 Normal, and 4273 Viral Pneumonia (VP) images for CXR. This study hypothesized that the proposed approach might be able to extract specific graphical features of COVID-19 and provide a clinical diagnosis ahead of the pathogenic test. Experimental results achieved in binary classification a high: Val_accuracy = 99.87% and 98.41%, Test_accuracy = 100% and 99.21%, Test_time = 0.002 s and 0.008 s per image for CT scans and CXR images, respectively, and in multi-classification: Val_accuracy = 97.48%, Test_accuracy = 92.96% with Test_time = 0.006 s per image for CXR images.

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Data Availability

The datasets generated during the current study are available in the kaggle repository: https://www.kaggle.com/datasets/prashant268/chest-xray-covid19-pneumonia, https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database and in the link: http://ictcf.biocuckoo.cn/Resource.php.

Abbreviations

AI:

Artificial intelligence

CT:

Computed Tomography Scan

CXR:

Chest X-ray

CV:

Computer Vision

CNN:

Convolutional Neural Networks

DL:

Deep learning

ML:

Machine learning

TL:

Transfer learning

AE:

Auto-encoder

SE:

Stacked encoder

SAE:

Stacked auto-encoder

SDAE:

Stacked Denoising auto-encoders

ANN:

Artificial Neural Network

SSDAE:

Sparse Stacked Denoising AE

References

  1. Farhat, H., Sakr, G.E., Kilany, R.: Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19. Mach. Vis. Appl. (2020). https://doi.org/10.1007/s00138-020-01101-5

    Article  PubMed Central  PubMed  Google Scholar 

  2. Ibrahim, D.M., Elshennawy, N.M., Sarhan, A.M.: Deep-chest: multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases. Comput. Biol. Med. 132, 104348 (2021). https://doi.org/10.1016/j.compbiomed.2021.104348

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  3. Mansour, R.F., Escorcia-Gutierrez, J., Gamarra, M., Gupta, D., Castillo, O., Kumar, S.: Unsupervised deep learning based variational autoencoder model for COVID-19 diagnosis and classification. Pattern Recogn. Lett. 151, 267–274 (2021). https://doi.org/10.1016/j.patrec.2021.08.018

    Article  ADS  Google Scholar 

  4. Loey M, Manogaran G & Khalifa NEM (2020). A deep transfer learning model with classical data augmentation and CGAN to detect COVID-19 from chest CT radiography digital images. Neural Comput. Appl. (2020). https://doi.org/10.1007/s00521-020-05437-x

  5. Muralidharan, N., Gupta, S., Prusty, M.R., Tripathy, R.K.: Detection of COVID19 from X-ray images using multiscale Deep Convolutional Neural Network. Appl. Soft Comput. 119, 108610 (2022). https://doi.org/10.1016/j.asoc.2022.108610

    Article  PubMed Central  PubMed  Google Scholar 

  6. Subramanian, N., Elharrouss, O., Al-Maadeed, S., Chowdhury, M.: A review of deep learning-based detection methods for COVID-19. Comput. Biol. Med. 143, 105233 (2022). https://doi.org/10.1016/j.compbiomed.2022.105233

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  7. El Gannour, O., Hamida, S., Cherradi, B., Al-Sarem, M., Raihani, A., Saeed, F., Hadwan, M.: Concatenation of pre-trained convolutional neural networks for enhanced COVID-19 screening using transfer learning technique. Electronics 11(1), 103 (2021). https://doi.org/10.3390/electronics11010103

    Article  CAS  Google Scholar 

  8. Aggarwal, P., Mishra, N.K., Fatimah, B., Singh, P., Gupta, A., Joshi, S.D.: COVID-19 image classification using deep learning: advances, challenges and opportunities. Comput. Biol. Med. 144, 105350 (2022). https://doi.org/10.1016/j.compbiomed.2022.105350

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  9. Das, A.K., Ghosh, S., Thunder, S., Dutta, R., Agarwal, S., Chakrabarti, A.: Automatic COVID-19 detection from X-ray images using ensemble learning with convolutional neural network. Pattern Anal. Appl. 24(3), 1111–1124 (2021). https://doi.org/10.1007/s10044-021-00970-4

    Article  Google Scholar 

  10. Lahsaini, I., El Habib Daho, M., Chikh, M.A.: Deep transfer learning based classification model for covid-19 using chest CT-scans. Pattern Recogn. Lett. 152, 122–128 (2021). https://doi.org/10.1016/j.patrec.2021.08.035

    Article  ADS  Google Scholar 

  11. Khanday, N.Y., Sofi, S.A.: Deep insight: convolutional neural network and its applications for COVID-19 prognosis. Biomed. Signal Process. Control 69, 102814 (2021). https://doi.org/10.1016/j.bspc.2021.102814

    Article  PubMed Central  PubMed  Google Scholar 

  12. Hasan, Md.K., Alam, Md.A., Dahal, L., Roy, S., Wahid, S.R., Elahi, Md.T.E., Martí, R., Khanal, B.: Challenges of deep learning methods for COVID-19 detection using public datasets. Inform. Med. Unlocked 30, 100945 (2022). https://doi.org/10.1016/j.imu.2022.100945

    Article  PubMed Central  PubMed  Google Scholar 

  13. Kaya, M., Eris, M.: D3SENet: A hybrid deep feature extraction network for Covid-19 classification using chest X-ray images. Biomed. Signal Process. Control 82, 104559 (2023). https://doi.org/10.1016/j.bspc.2022.104559

    Article  PubMed Central  PubMed  Google Scholar 

  14. Nasir, N., Kansal, A., Barneih, F., Al-Shaltone, O., Bonny, T., Al-Shabi, M., Al Shammaa, A.: Multi-modal image classification of COVID-19 cases using computed tomography and X-rays scans. Intell. Syst. Appl. 17, 200160 (2023). https://doi.org/10.1016/j.iswa.2022.200160

    Article  Google Scholar 

  15. Khan, S.H., Sohail, A., Khan, A., Hassan, M., Lee, Y.S., Alam, J., Basit, A., Zubair, S.: COVID-19 detection in chest X-ray images using deep boosted hybrid learning. Comput. Biol. Med. 137, 104816 (2021). https://doi.org/10.1016/j.compbiomed.2021.104816

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  16. Dilshad, S., Singh, N., Atif, M., Hanif, A., Yaqub, N., Farooq, W.A., Ahmad, H., Chu, Y., Masood, M.T.: Automated image classification of chest X-rays of COVID-19 using deep transfer learning. Results Phys. 28, 104529 (2021). https://doi.org/10.1016/j.rinp.2021.104529

    Article  PubMed Central  PubMed  Google Scholar 

  17. Hertel, R., Benlamri, R.: COV-SNET: a deep learning model for X-ray-based COVID-19 classification. Inform. Med. Unlocked 24, 100620 (2021). https://doi.org/10.1016/j.imu.2021.100620

    Article  PubMed Central  PubMed  Google Scholar 

  18. Kogilavani, S.V., Prabhu, J., Sandhiya, R., Kumar, M.S., Subramaniam, U., Karthick, A., Muhibbullah, M., Imam, S.B.S.: COVID-19 detection based on lung Ct scan using deep learning techniques. Comput. Math. Methods Med. 2022, 7672196 (2022). https://doi.org/10.1155/2022/7672196

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  19. Li, D., Fu, Z., Xu, J.: Stacked-autoencoder-based model for COVID-19 diagnosis on CT images. Appl. Intell. (Dordrecht, Netherlands) 51(5), 2805–2817 (2021). https://doi.org/10.1007/s10489-020-02002-w

    Article  Google Scholar 

  20. Madhavan, M.V., Khamparia, A., Gupta, D., Pande, S., Tiwari, P., Hossain, M.S.: Res-CovNet: an internet of medical health things driven COVID-19 framework using transfer learning. Neural Comput. Appl. 35(19), 13907–13920 (2023). https://doi.org/10.1007/s00521-021-06171-8

    Article  PubMed  Google Scholar 

  21. Ullah, Z., Usman, M., Gwak, J.: MTSS-AAE: multi-task semi-supervised adversarial autoencoding for COVID-19 detection based on chest X-ray images. Expert Syst. Appl. 216, 119475 (2023). https://doi.org/10.1016/j.eswa.2022.119475

    Article  PubMed Central  PubMed  Google Scholar 

  22. Addo, D., Zhou, S., Jackson, J.K., Nneji, G.U., Monday, H.N., Sarpong, K., Patamia, R.A., Ekong, F., Owusu-Agyei, C.A.: EVAE-net: an ensemble variational autoencoder deep learning network for COVID-19 classification based on chest X-ray images. Diagnostics (Basel, Switzerland) 12(11), 2569 (2022). https://doi.org/10.3390/diagnostics12112569

    Article  PubMed  Google Scholar 

  23. Reddy, A.S.K., Rao, K.N.B., Soora, N.R., Shailaja, K., Kumar, N.C.S., Sridharan, A., Uthayakumar, J.: Multi-modal fusion of deep transfer learning based COVID-19 diagnosis and classification using chest x-ray images. Multimed. Tools Appl. 82(8), 12653–12677 (2023). https://doi.org/10.1007/s11042-022-13739-6

    Article  PubMed  Google Scholar 

  24. Abdulkareem, K.H., Mostafa, S.A., Al-Qudsy, Z.N., Mohammed, M.A., Al-Waisy, A.S., Kadry, S., Lee, J., Nam, Y.: Automated system for identifying COVID-19 infections in computed tomography images using deep learning models. J. Healthcare Eng. 2022, 1–13 (2022). https://doi.org/10.1155/2022/5329014

    Article  Google Scholar 

  25. Demir, F., Demir, K., Şengür, A.: DeepCov19Net: automated COVID-19 disease detection with a robust and effective technique deep learning approach. N. Gener. Comput. 40(4), 1053–1075 (2022). https://doi.org/10.1007/s00354-021-00152-0

    Article  Google Scholar 

  26. Khan, A.I., Shah, J.L., Bhat, M.M.: CoroNet: a deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Comput. Methods Programs Biomed. 196, 105581 (2020). https://doi.org/10.1016/j.cmpb.2020.105581

    Article  PubMed Central  PubMed  Google Scholar 

  27. Loey, M., El-Sappagh, S., Mirjalili, S.: Bayesian-based optimized deep learning model to detect COVID-19 patients using chest X-ray image data. Comput. Biol. Med. 142, 105213 (2022). https://doi.org/10.1016/j.compbiomed.2022.105213

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  28. Khan, E., Rehman, M.Z.U., Ahmed, F., Alfouzan, F.A., Alzahrani, N.M., Ahmad, J.: Chest X-ray classification for the detection of COVID-19 using deep learning techniques. Sensors (Basel, Switzerland) 22(3), 1211 (2022). https://doi.org/10.3390/s22031211

    Article  ADS  CAS  PubMed  Google Scholar 

  29. Bhattacharyya, A., Bhaik, D., Kumar, S., Thakur, P., Sharma, R., Pachori, R.B.: A deep learning based approach for automatic detection of COVID-19 cases using chest X-ray images. Biomed. Signal Process. Control 71, 103182 (2022). https://doi.org/10.1016/j.bspc.2021.103182

    Article  PubMed  Google Scholar 

  30. Wang, D., Mo, J., Zhou, G., Xu, L., Liu, Y.: An efficient mixture of deep and machine learning models for COVID-19 diagnosis in chest X-ray images. PLoS One 15(11), e0242535 (2020). https://doi.org/10.1371/journal.pone.0242535

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  31. Shukla, P.K., Sandhu, J.K., Ahirwar, A., Ghai, D., Maheshwary, P., Shukla, P.K.: Multiobjective genetic algorithm and convolutional neural network based COVID-19 identification in chest X-ray images. Math. Probl. Eng. 2021, 1–9 (2021). https://doi.org/10.1155/2021/7804540

    Article  CAS  Google Scholar 

  32. Sitaula, C., Hossain, M.B.: Attention-based VGG-16 model for COVID-19 chest X-ray image classification. Appl. Intell. 51(5), 2850–2863 (2020). https://doi.org/10.1007/s10489-020-02055-x

    Article  Google Scholar 

  33. Wang, W., Li, Y., Zou, T., Wang, X., You, J., Luo, Y.: A novel image classification approach via Dense-MobileNet models. Mob. Inf. Syst. 2020, 1–8 (2020). https://doi.org/10.1155/2020/7602384

    Article  Google Scholar 

  34. Srinivasan, K., Garg, L., Datta, D., Alaboudi, A.A., Jhanjhi, N.Z., Agarwal, R., George-Thomas, A.: Performance comparison of deep CNN models for detecting driver’s distraction. Comput. Mater. Continua 68(3), 4109–4124 (2021). https://doi.org/10.32604/cmc.2021.016736

    Article  Google Scholar 

  35. Verma, P., Tripathi, V., Pant, B.: Comparison of different optimizers implemented on the deep learning architectures for COVID-19 classification. Mater. Today: Proc. 46, 11098–11102 (2021). https://doi.org/10.1016/j.matpr.2021.02.244

    Article  CAS  PubMed  Google Scholar 

  36. Siddalingappa, R., Kanagaraj, S.: Anomaly detection on medical images using autoencoder and convolutional neural network. Int. J. Adv. Comput. Sci. Appl. (2021). https://doi.org/10.14569/ijacsa.2021.0120717

    Article  Google Scholar 

  37. Rashid, N., Hossain, M.A.F., Ali, M., Islam Sukanya, M., Mahmud, T., Fattah, S.A.: AutoCovNet: Unsupervised feature learning using autoencoder and feature merging for detection of COVID-19 from chest X-ray images. Biocybern. Biomed. Eng. 41(4), 1685–1701 (2021). https://doi.org/10.1016/j.bbe.2021.09.004

    Article  PubMed Central  PubMed  Google Scholar 

  38. Agarwal, N., Mohanty, S.N., Sankhwar, S., Dash, J.K.: A Novel model to predict the effects of enhanced students’ computer interaction on their health in COVID-19 pandemics. N. Gener. Comput. 41(3), 635–668 (2023). https://doi.org/10.1007/s00354-023-00224-3

    Article  Google Scholar 

  39. Rahimzadeh, M., Attar, A.: A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2. Inform. Med. Unlocked 19, 100360 (2020). https://doi.org/10.1016/j.imu.2020.100360

    Article  PubMed Central  PubMed  Google Scholar 

  40. Narin, A., Kaya, C., Pamuk, Z.: Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. Pattern Anal. Appl. 24(3), 1207–1220 (2021). https://doi.org/10.1007/s10044-021-00984-y

    Article  PubMed Central  PubMed  Google Scholar 

  41. Kong, L., Cheng, J.: Classification and detection of COVID-19 X-Ray images based on DenseNet and VGG16 feature fusion. Biomed. Signal Process. Control 77, 103772 (2022). https://doi.org/10.1016/j.bspc.2022.103772

    Article  PubMed Central  PubMed  Google Scholar 

  42. Xu, Y., Lam, H.-K., Jia, G.: MANet: A two-stage deep learning method for classification of COVID-19 from Chest X-ray images. Neurocomputing 443, 96–105 (2021). https://doi.org/10.1016/j.neucom.2021.03.034

    Article  PubMed Central  PubMed  Google Scholar 

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Acknowledgements

This research was carried out with the support of the institutions RB_IAIM, LI3C, M.Khider Biskra University, Algeria.

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Authors and Affiliations

  1. RB_IAIM, LI3C, M. Khider Biskra University, Biskra, Algeria

    Meryem Ketfi & Mebarka Belahcene

  2. GSM Fresnel Institute and Ecole Centrale Marseille, Marseille, France

    Salah Bourennane

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Ketfi, M., Belahcene, M. & Bourennane, S. Transfer Learning Fusion and Stacked Auto-encoders for Viral Lung Disease Classification. New Gener. Comput. (2024). https://doi.org/10.1007/s00354-024-00247-4

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