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

被引:37
作者
Alsharif, M. H. [1 ]
Alsharif, Y. H. [2 ]
Yahya, K. [3 ]
Alomari, O. A. [4 ]
Albreem, M. A. [5 ]
Jahid, A. [6 ]
机构
[1] Sejong Univ, Coll Elect & Informat Engn, Dept Elect Engn, Seoul, South Korea
[2] Islamic Univ Gaza, Fac Med, Gaza, Palestine
[3] Istanbul Gelisim Univ, Fac Engn & Architecture, Mechatron Engn Dept, Istanbul, Turkey
[4] Istanbul Gelisim Univ, Fac Engn & Architecture, Dept Comp Engn, Istanbul, Turkey
[5] ASharqiyah Univ, Dept Elect & Commun Engn, Ibra, Oman
[6] Univ Ottawa, Dept Elect & Comp Engn, Ottawa, ON, Canada
关键词
Artificial intelligence; Coronavirus pandemic; AI; SARS-CoV-2; Machine learning; Big data; COVID-19;
D O I
10.26355/eurrev_202011_23640
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
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 (RTPCR) 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.
引用
收藏
页码:11455 / 11460
页数:6
相关论文
共 33 条
[1]  
[Anonymous], 2014, ARXIV14091556
[2]  
[Anonymous], 2015, IEEE C COMPUTER VISI
[3]   Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks [J].
Apostolopoulos, Ioannis D. ;
Mpesiana, Tzani A. .
PHYSICAL AND ENGINEERING SCIENCES IN MEDICINE, 2020, 43 (02) :635-640
[4]  
Barstugan M, 2020, OZTURK CORONAVIRUS C
[5]   Deep-learning framework to detect lung abnormality - A study with chest X-Ray and lung CT scan images [J].
Bhandary, Abhir ;
Prabhu, G. Ananth ;
Rajinikanth, V ;
Thanaraj, K. Palani ;
Satapathy, Suresh Chandra ;
Robbins, David E. ;
Shasky, Charles ;
Zhang, Yu-Dong ;
Tavares, Joao Manuel R. S. ;
Raja, N. Sri Madhava .
PATTERN RECOGNITION LETTERS, 2020, 129 :271-278
[6]   RETRACTED: Deep learning system to screen coronavirus disease 2019 pneumonia (Retracted Article) [J].
Butt, Charmaine ;
Gill, Jagpal ;
Chun, David ;
Babu, Benson A. .
APPLIED INTELLIGENCE, 2023, 53 (04) :4874-4874
[7]   Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR (Publication with Expression of Concern) [J].
Corman, Victor M. ;
Landt, Olfert ;
Kaiser, Marco ;
Molenkamp, Richard ;
Meijer, Adam ;
Chu, Daniel K. W. ;
Bleicker, Tobias ;
Bruenink, Sebastian ;
Schneider, Julia ;
Schmidt, Marie Luisa ;
Mulders, Daphne G. J. C. ;
Haagmans, Bart L. ;
van der Veer, Bas ;
van den Brink, Sharon ;
Wijsman, Lisa ;
Goderski, Gabriel ;
Romette, Jean-Louis ;
Ellis, Joanna ;
Zambon, Maria ;
Peiris, Malik ;
Goossens, Herman ;
Reusken, Chantal ;
Koopmans, Marion P. G. ;
Drosten, Christian .
EUROSURVEILLANCE, 2020, 25 (03) :23-30
[8]  
FOMSGAARD A, 2020, EUROSURVEILLANCE, V25
[9]  
Goodfellow IJ, 2014, ADV NEUR IN, V27, P2672
[10]  
Gozes O., 2020, CORONAVIRUS DETECTIO