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

R. Elakkiya (), Pandi Vijayakumar () and Marimuthu Karuppiah ()
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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

Information Systems Frontiers, 2021, vol. 23, issue 6, No 2, 1369-1383

Abstract: 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.

Keywords: Deep learning; COVID-19; AI diagnostics tool; Diagnostic radiography; Machine learning; Medical diagnosis; X-rays (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10796-021-10123-x

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