Version 1
: Received: 7 August 2020 / Approved: 8 August 2020 / Online: 8 August 2020 (18:24:58 CEST)
Version 2
: Received: 17 October 2020 / Approved: 19 October 2020 / Online: 19 October 2020 (10:49:25 CEST)
Rahman, S., Sarker, S., Miraj, M.A.A. et al. Deep Learning–Driven Automated Detection of COVID-19 from Radiography Images: a Comparative Analysis. Cogn Comput (2021). https://doi.org/10.1007/s12559-020-09779-5
Rahman, S., Sarker, S., Miraj, M.A.A. et al. Deep Learning–Driven Automated Detection of COVID-19 from Radiography Images: a Comparative Analysis. Cogn Comput (2021). https://doi.org/10.1007/s12559-020-09779-5
Cite as:
Rahman, S., Sarker, S., Miraj, M.A.A. et al. Deep Learning–Driven Automated Detection of COVID-19 from Radiography Images: a Comparative Analysis. Cogn Comput (2021). https://doi.org/10.1007/s12559-020-09779-5
Rahman, S., Sarker, S., Miraj, M.A.A. et al. Deep Learning–Driven Automated Detection of COVID-19 from Radiography Images: a Comparative Analysis. Cogn Comput (2021). https://doi.org/10.1007/s12559-020-09779-5
Abstract
The COVID-19 pandemic has wreaked havoc on the whole world, taking over half a million lives and capsizing the world economy in unprecedented magnitudes. With the world scampering for a vaccine, early detection and containment is the only redress. Existing diagnostic technologies with high accuracy like RT-PCRs are expensive and sophisticated, requiring skilled individuals for specimen collection and screening, resulting in lower outreach. So, methods excluding direct human intervention are much sought after, and artificial intelligence-driven automated diagnosis, especially with radiography images, captured the researchers' interest. This survey marks a detailed inspection of the deep-learning-based automated detection of COVID-19 works done to date, a comparison of the available datasets, methodical challenges like imbalanced datasets, and others, along with probable solutions with different pre-processing methods, and scopes of future exploration in this arena. We also benchmarked the performance of 315 deep models in diagnosing COVID-19, Normal, and Pneumonia from x-ray images of a custom dataset created from four others. The dataset is publicly available at https://github.com/rgbnihal2/COVID-19-X-ray-Dataset. Our results show that DenseNet201 model with Quadratic SVM classifier performs the best (accuracy: 98.16%, sensitivity: 98.93%, specificity: 98.77%) and maintains high accuracies in other similar architectures as well. This proves that even though radiography images might not be conclusive for radiologists, but it is so for deep learning algorithms for detecting COVID-19. We hope this extensive review will provide a comprehensive guideline for researchers in this field.
COVID-19; deep learning; radiography; automated detection; medical imaging; SARS-CoV-2
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received:
19 October 2020
Commenter:
Sejuti Rahman
Commenter's Conflict of Interests:
Author
Comment:
This is the revised and accepted version of the paper that addresses all comments and issues raised by the reviewers of the Cognitive Computation Journal. In the revised paper, we included a new section on Image Preprocessing in Section 7. We also added two new tables (Table 4 and Table 6), one figure (Figure 4), and revised the content of Tables 1, 2, & 7 and Figure 5. We also modified the abstract, dataset description in Experiments, and the conclusion.
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Commenter: Sejuti Rahman
Commenter's Conflict of Interests: Author