Literature DB >> 33215473

Deep learning applications to combat the dissemination of COVID-19 disease: a review.

M H Alsharif1, Y H Alsharif, K Yahya, O A Alomari, M A Albreem, A Jahid.   

Abstract

Recent Coronavirus (COVID-19) is one of the respiratory diseases, and it is known as fast infectious ability. This dissemination can be decelerated by diagnosing and quarantining patients with COVID-19 at early stages, thereby saving numerous lives. Reverse transcription-polymerase chain reaction (RT-PCR) is known as one of the primary diagnostic tools. However, RT-PCR tests are costly and time-consuming; it also requires specific materials, equipment, and instruments. Moreover, most countries are suffering from a lack of testing kits because of limitations on budget and techniques. Thus, this standard method is not suitable to meet the requirements of fast detection and tracking during the COVID-19 pandemic, which motived to employ deep learning (DL)/convolutional neural networks (CNNs) technology with X-ray and CT scans for efficient analysis and diagnostic. This study provides insight about the literature that discussed the deep learning technology and its various techniques that are recently developed to combat the dissemination of COVID-19 disease.

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Year:  2020        PMID: 33215473     DOI: 10.26355/eurrev_202011_23640

Source DB:  PubMed          Journal:  Eur Rev Med Pharmacol Sci        ISSN: 1128-3602            Impact factor:   3.507


  10 in total

Review 1.  Exploring the Deep-Learning Techniques in Detecting the Presence of Coronavirus in the Chest X-Ray Images: A Comprehensive Review.

Authors:  K Silpaja Chandrasekar
Journal:  Arch Comput Methods Eng       Date:  2022-05-23       Impact factor: 8.171

Review 2.  The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions.

Authors:  Arash Heidari; Nima Jafari Navimipour; Mehmet Unal; Shiva Toumaj
Journal:  Comput Biol Med       Date:  2021-12-14       Impact factor: 6.698

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

Authors:  R Elakkiya; Pandi Vijayakumar; Marimuthu Karuppiah
Journal:  Inf Syst Front       Date:  2021-03-17       Impact factor: 6.191

4.  Remotely Monitoring COVID-19 Patient Health Condition Using Metaheuristics Convolute Networks from IoT-Based Wearable Device Health Data.

Authors:  Mustafa Musa Jaber; Thamer Alameri; Mohammed Hasan Ali; Adi Alsyouf; Mohammad Al-Bsheish; Badr K Aldhmadi; Sarah Yahya Ali; Sura Khalil Abd; Saif Mohammed Ali; Waleed Albaker; Mu'taman Jarrar
Journal:  Sensors (Basel)       Date:  2022-02-05       Impact factor: 3.576

5.  Integrated model for COVID-19 diagnosis based on computed tomography artificial intelligence, and clinical features: a multicenter cohort study.

Authors:  Yuki Kataoka; Yuya Kimura; Tatsuyoshi Ikenoue; Yoshinori Matsuoka; Junichi Matsumoto; Junji Kumasawa; Kentaro Tochitatni; Hiraku Funakoshi; Tomohiro Hosoda; Aiko Kugimiya; Michinori Shirano; Fumiko Hamabe; Sachiyo Iwata; Shingo Fukuma
Journal:  Ann Transl Med       Date:  2022-02

Review 6.  Radiological Analysis of COVID-19 Using Computational Intelligence: A Broad Gauge Study.

Authors:  S Vineth Ligi; Soumya Snigdha Kundu; R Kumar; R Narayanamoorthi; Khin Wee Lai; Samiappan Dhanalakshmi
Journal:  J Healthc Eng       Date:  2022-02-23       Impact factor: 2.682

7.  Deep learning empowered COVID-19 diagnosis using chest CT scan images for collaborative edge-cloud computing platform.

Authors:  Vipul Kumar Singh; Maheshkumar H Kolekar
Journal:  Multimed Tools Appl       Date:  2021-06-28       Impact factor: 2.577

8.  Lessons from the COVID-19 Pandemic on the Use of Artificial Intelligence in Digital Radiology: The Submission of a Survey to Investigate the Opinion of Insiders.

Authors:  Daniele Giansanti; Ivano Rossi; Lisa Monoscalco
Journal:  Healthcare (Basel)       Date:  2021-03-15

9.  The Artificial Intelligence in Digital Radiology: Part 2: Towards an Investigation of acceptance and consensus on the Insiders.

Authors:  Francesco Di Basilio; Gianluca Esposisto; Lisa Monoscalco; Daniele Giansanti
Journal:  Healthcare (Basel)       Date:  2022-01-14

Review 10.  The Artificial Intelligence in Digital Radiology: Part 1: The Challenges, Acceptance and Consensus.

Authors:  Daniele Giansanti; Francesco Di Basilio
Journal:  Healthcare (Basel)       Date:  2022-03-10
  10 in total

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