Automated detection and forecasting of COVID-19 using deep learning techniques: A review
Article
Shoeibi, Afshin, Khodatars, Marjane, Jafari, Mahboobeh, Ghassemi, Navid, Sadeghi, Delaram, Moridian, Parisa, Khadem, Ali, Alizadehsani, Roohallah, Hussain, Sadiq, Zare, Assef, Sani, Zahra Alizadeh, Khozeimeh, Fahime, Nahavandi, Saeid, Acharya, U. Rajendra and Gorriz, Juan M.. 2024. "Automated detection and forecasting of COVID-19 using deep learning techniques: A review." Neurocomputing. 577. https://doi.org/10.1016/j.neucom.2024.127317
| Article Title | Automated detection and forecasting of COVID-19 using deep learning techniques: A review |
|---|---|
| ERA Journal ID | 18092 |
| Article Category | Article |
| Authors | Shoeibi, Afshin, Khodatars, Marjane, Jafari, Mahboobeh, Ghassemi, Navid, Sadeghi, Delaram, Moridian, Parisa, Khadem, Ali, Alizadehsani, Roohallah, Hussain, Sadiq, Zare, Assef, Sani, Zahra Alizadeh, Khozeimeh, Fahime, Nahavandi, Saeid, Acharya, U. Rajendra and Gorriz, Juan M. |
| Journal Title | Neurocomputing |
| Journal Citation | 577 |
| Article Number | 127317 |
| Number of Pages | 32 |
| Year | 2024 |
| Publisher | Elsevier |
| Place of Publication | Netherlands |
| ISSN | 0925-2312 |
| 1872-8286 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.neucom.2024.127317 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0925231224000882 |
| Abstract | In March 2020, the World Health Organization (WHO) declared COVID-19 a global epidemic, caused by the SARS-CoV-2 virus. Initially, COVID-19 was diagnosed using real-time reverse transcription–polymerase chain reaction (RT-PCR) tests with a turnaround time of 2–3 days. To enhance diagnostic accuracy, medical professionals use medical imaging alongside RT-PCR. A positive result on both RT-PCR and medical imaging confirms a COVID-19 diagnosis. Imaging modalities like chest X-ray (CXR), computed tomography (CT) scans, and ultrasound are widely utilized for rapid and precise COVID-19 diagnoses. However, interpreting COVID-19 from these images is time-consuming and susceptible to human error. Therefore, leveraging artificial intelligence (AI) methods, particularly deep learning (DL) models, can deliver consistent, high-performance results. Unlike conventional machine learning (ML), DL models automate all stages of feature extraction, selection, and classification. This paper presents a comprehensive review of using DL techniques for diagnosing COVID-19 from medical imaging. The introduction provides an overview of diagnosing the coronavirus using medical imaging, highlighting associated challenges. Subsequently, the paper delves into key aspects of Computer-Aided Diagnosis Systems (CADS) based on DL methods for diagnosing COVID-19, covering segmentation, classification, explainable AI (XAI), and predictive research. Additionally, it reviews the rehabilitation systems such as the Internet of Medical Things (IoMT) in the context of COVID-19. In another section, uncertainty quantification (UQ) research is showcased, focusing on DL models for the diagnosis of Covid-19. Crucial challenges and future research directions are outlined in another section. Finally, discussion and conclusion sections are also provided at the end of the paper. |
| Keywords | COVID-19; Diagnosis; Deep Learning ; Explainable AI ; Rehabilitation Systems ; Uncertainty Quantification |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 420311. Health systems |
| Public Notes | The accessible file is the accepted version of the paper. Please refer to the URL for the published version. |
| Byline Affiliations | University of Granada, Spain |
| Ferdowsi University of Mashhad, Iran | |
| K. N. Toosi University of Technology, Iran | |
| Deakin University | |
| Dibrugarh University, India | |
| Islamic Azad University, Iran | |
| Iran University of Medical Sciences, Iran | |
| Swinburne University of Technology | |
| School of Mathematics, Physics and Computing | |
| University of Cambridge, United Kingdom |
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