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COVID_SCREENET: COVID-19 Screening in Chest Radiography Images Using Deep Transfer Stacking

Author

Listed:
  • R. Elakkiya

    (SASTRA Deemed To Be University)

  • Pandi Vijayakumar

    (University College of Engineering Tindivanam)

  • Marimuthu Karuppiah

    (SRM Institute of Science and Technology, Delhi- NCR Campus)

Abstract

Infectious diseases are highly contagious due to rapid transmission and very challenging to diagnose in the early stage. Artificial Intelligence and Machine Learning now become a strategic weapon in assisting infectious disease prevention, rapid-response in diagnosis, surveillance, and management. In this paper, a bifold COVID_SCREENET architecture is introduced for providing COVID-19 screening solutions using Chest Radiography (CR) images. Transfer learning using nine pre-trained ImageNet models to extract the features of Normal, Pneumonia, and COVID-19 images is adapted in the first fold and classified using baseline Convolutional Neural Network (CNN). A Modified Stacked Ensemble Learning (MSEL) is proposed in the second fold by stacking the top five pre-trained models, and then the predictions resulted. Experimentation is carried out in two folds: In first fold, open-source samples are considered and in second fold 2216 real-time samples collected from Tamilnadu Government Hospitals, India, and the screening results for COVID data is 100% accurate in both the cases. The proposed approach is also validated and blind reviewed with the help of two radiologists at Thanjavur Medical College & Hospitals by collecting 2216 chest X-ray images between the month of April and May. Based on the reports, the measures are calculated for COVID_SCREENET and it showed 100% accuracy in performing multi-class classification.

Suggested Citation

  • R. Elakkiya & Pandi Vijayakumar & Marimuthu Karuppiah, 2021. "COVID_SCREENET: COVID-19 Screening in Chest Radiography Images Using Deep Transfer Stacking," Information Systems Frontiers, Springer, vol. 23(6), pages 1369-1383, December.
    • Handle: RePEc:spr:infosf:v:23:y:2021:i:6:d:10.1007_s10796-021-10123-x
    DOI: 10.1007/s10796-021-10123-x
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    References listed on IDEAS

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    1. Haiman Tian & Shu-Ching Chen & Mei-Ling Shyu, 2020. "Evolutionary Programming Based Deep Learning Feature Selection and Network Construction for Visual Data Classification," Information Systems Frontiers, Springer, vol. 22(5), pages 1053-1066, October.
    2. Mohammed Kuko & Mohammad Pourhomayoun, 2020. "Single and Clustered Cervical Cell Classification with Ensemble and Deep Learning Methods," Information Systems Frontiers, Springer, vol. 22(5), pages 1039-1051, October.
    3. DunLu Peng & YinRui Wang & Cong Liu & Zhang Chen, 0. "TL-NER: A Transfer Learning Model for Chinese Named Entity Recognition," Information Systems Frontiers, Springer, vol. 0, pages 1-14.
    4. Mohammed Kuko & Mohammad Pourhomayoun, 0. "Single and Clustered Cervical Cell Classification with Ensemble and Deep Learning Methods," Information Systems Frontiers, Springer, vol. 0, pages 1-13.
    5. Haiman Tian & Shu-Ching Chen & Mei-Ling Shyu, 0. "Evolutionary Programming Based Deep Learning Feature Selection and Network Construction for Visual Data Classification," Information Systems Frontiers, Springer, vol. 0, pages 1-14.
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    Cited by:

    1. Victor Chang & Carole Goble & Muthu Ramachandran & Lazarus Jegatha Deborah & Reinhold Behringer, 2021. "Editorial on Machine Learning, AI and Big Data Methods and Findings for COVID-19," Information Systems Frontiers, Springer, vol. 23(6), pages 1363-1367, December.

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