AI for COVID-19 Detection from Radiographs: Incisive Analysis of State of the Art Techniques, Key Challenges and Future Directions.
Journal
Ingenierie et recherche biomedicale : IRBM = Biomedical engineering and research
ISSN: 1959-0318
Titre abrégé: Ing Rech Biomed
Pays: Netherlands
ID NLM: 101475162
Informations de publication
Date de publication:
Oct 2022
Oct 2022
Historique:
received:
15
02
2021
revised:
14
06
2021
accepted:
19
07
2021
pubmed:
3
8
2021
medline:
3
8
2021
entrez:
2
8
2021
Statut:
ppublish
Résumé
In recent years, Artificial Intelligence has had an evident impact on the way research addresses challenges in different domains. It has proven to be a huge asset, especially in the medical field, allowing for time-efficient and reliable solutions. This research aims to spotlight the impact of deep learning and machine learning models in the detection of COVID-19 from medical images. This is achieved by conducting a review of the state-of-the-art approaches proposed by the recent works in this field. The main focus of this study is the recent developments of classification and segmentation approaches to image-based COVID-19 detection. The study reviews 140 research papers published in different academic research databases. These papers have been screened and filtered based on specified criteria, to acquire insights prudent to image-based COVID-19 detection. The methods discussed in this review include different types of imaging modality, predominantly X-rays and CT scans. These modalities are used for classification and segmentation tasks as well. This review seeks to categorize and discuss the different deep learning and machine learning architectures employed for these tasks, based on the imaging modality utilized. It also hints at other possible deep learning and machine learning architectures that can be proposed for better results towards COVID-19 detection. Along with that, a detailed overview of the emerging trends and breakthroughs in Artificial Intelligence-based COVID-19 detection has been discussed as well. This work concludes by stipulating the technical and non-technical challenges faced by researchers and illustrates the advantages of image-based COVID-19 detection with Artificial Intelligence techniques.
Sections du résumé
Background and objective
UNASSIGNED
In recent years, Artificial Intelligence has had an evident impact on the way research addresses challenges in different domains. It has proven to be a huge asset, especially in the medical field, allowing for time-efficient and reliable solutions. This research aims to spotlight the impact of deep learning and machine learning models in the detection of COVID-19 from medical images. This is achieved by conducting a review of the state-of-the-art approaches proposed by the recent works in this field.
Methods
UNASSIGNED
The main focus of this study is the recent developments of classification and segmentation approaches to image-based COVID-19 detection. The study reviews 140 research papers published in different academic research databases. These papers have been screened and filtered based on specified criteria, to acquire insights prudent to image-based COVID-19 detection.
Results
UNASSIGNED
The methods discussed in this review include different types of imaging modality, predominantly X-rays and CT scans. These modalities are used for classification and segmentation tasks as well. This review seeks to categorize and discuss the different deep learning and machine learning architectures employed for these tasks, based on the imaging modality utilized. It also hints at other possible deep learning and machine learning architectures that can be proposed for better results towards COVID-19 detection. Along with that, a detailed overview of the emerging trends and breakthroughs in Artificial Intelligence-based COVID-19 detection has been discussed as well.
Conclusion
UNASSIGNED
This work concludes by stipulating the technical and non-technical challenges faced by researchers and illustrates the advantages of image-based COVID-19 detection with Artificial Intelligence techniques.
Identifiants
pubmed: 34336141
doi: 10.1016/j.irbm.2021.07.002
pii: S1959-0318(21)00095-6
pmc: PMC8312058
doi:
Types de publication
Journal Article
Review
Langues
eng
Pagination
486-510Informations de copyright
© 2021 AGBM. Published by Elsevier Masson SAS. All rights reserved.
Déclaration de conflit d'intérêts
The authors declare that they have no known competing financial or personal relationships that could be viewed as influencing the work reported in this paper.
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