Certain Investigations on the Application of Machine learning Algorithms and Deep Learning Architectures for Covid-19 Diagnosis

被引:1
作者
Soundariya, R. S. [1 ]
Tharsanee, R. M. [2 ]
Vishnupriya, B. [3 ]
Ashwathi, R. [4 ]
Nivaashini, M. [5 ]
机构
[1] Bannari Amman Inst Technol, Dept Comp Sci & Engn, Sathyamangalam, Tamil Nadu, India
[2] Bannari Amman Inst Technol, Dept Comp Sci & Engn, Sathyamangalam, Tamil Nadu, India
[3] Bannari Amman Inst Technol, Dept Comp Sci & Engn, Sathyamangalam, Tamil Nadu, India
[4] Bannari Amman Inst Technol, Dept Civil Engn, Sathyamangalam, Tamil Nadu, India
[5] KPR Inst Engn & Technol, Dept Comp Sci & Engn, Sathyamangalam, Tamil Nadu, India
来源
JOURNAL OF ENGINEERING RESEARCH | 2021年 / 9卷
关键词
Covid-19; diagnosis; Machine Learning; Mathematical models; Deep Learning;
D O I
10.36909/jer.ICMMM.12421
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Corona virus disease (Covid - 19) has started to promptly spread worldwide from April 2020 till date, leading to massive death and loss of lives of people across various countries. In accordance to the advices of WHO, presently the diagnosis is implemented by Reverse Transcription Polymerase Chain Reaction (RT- PCR) testing, that incurs four to eight hours' time to process test samples and adds 48 hours to categorize whether the samples are positive or negative. It is obvious that laboratory tests are time consuming and hence a speedy and prompt diagnosis of the disease is extremely needed. This can be attained through several Artificial Intelligence methodologies for prior diagnosis and tracing of corona diagnosis. Those methodologies are summarized into three categories: (i) Predicting the pandemic spread using mathematical models (ii) Empirical analysis using machine learning models to forecast the global corona transition by considering susceptible, infected and recovered rate. (iii) Utilizing deep learning architectures for corona diagnosis using the input data in the form of X-ray images and CT scan images. When X-ray and CT scan images are taken into account, supplementary data like medical signs, patient history and laboratory test results can also be considered while training the learning model and to advance the testing efficacy. Thus the proposed investigation summaries the several mathematical models, machine learning algorithms and deep learning frameworks that can be executed on the datasets to forecast the traces of COVID-19 and detect the risk factors of coronavirus.
引用
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页数:15
相关论文
共 23 条
[1]   A Statistical Modeling of the Course of COVID-19 (SARS-CoV-2) Outbreak: A Comparative Analysis [J].
Ankarali, Handan ;
Ankaralli, Seyit ;
Caskurlu, Hulya ;
Cag, Yasemin ;
Arslan, Ferhat ;
Erdem, Hakan ;
Vahaboglu, Haluk .
ASIA-PACIFIC JOURNAL OF PUBLIC HEALTH, 2020, 32 (04) :157-160
[2]  
Arti M.K., 2020, MODELING PREDICTIONS
[3]   A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research-An International Collaboration [J].
Banda, Juan M. ;
Tekumalla, Ramya ;
Wang, Guanyu ;
Yu, Jingyuan ;
Liu, Tuo ;
Ding, Yuning ;
Artemova, Ekaterina ;
Tutubalina, Elena ;
Chowell, Gerardo .
EPIDEMIOLOGIA, 2021, 2 (03) :315-324
[4]  
Barstugan Mucahid, 2020, MACHINE LEARNING STA
[5]   A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study [J].
Chen, Xiaofeng ;
Tang, Yanyan ;
Mo, Yongkang ;
Li, Shengkai ;
Lin, Daiying ;
Yang, Zhijian ;
Yang, Zhiqi ;
Sun, Hongfu ;
Qiu, Jinming ;
Liao, Yuting ;
Xiao, Jianning ;
Chen, Xiangguang ;
Wu, Xianheng ;
Wu, Renhua ;
Dai, Zhuozhi .
EUROPEAN RADIOLOGY, 2020, 30 (09) :4893-4902
[6]  
Cohen J.P, 2020, arXiv preprint arXiv:2006.11988
[7]   Analyzing the epidemiological outbreak of COVID-19: A visual exploratory data analysis approach [J].
Dey, Samrat K. ;
Rahman, Md. Mahbubur ;
Siddiqi, Umme R. ;
Howlader, Arpita .
JOURNAL OF MEDICAL VIROLOGY, 2020, 92 (06) :632-638
[8]   An interactive web-based dashboard to track COVID-19 in real time [J].
Dong, Ensheng ;
Du, Hongru ;
Gardner, Lauren .
LANCET INFECTIOUS DISEASES, 2020, 20 (05) :533-534
[9]   New machine learning method for image-based diagnosis of COVID-19 [J].
Elaziz, Mohamed Abd ;
Hosny, Khalid M. ;
Salah, Ahmad ;
Darwish, Mohamed M. ;
Lu, Songfeng ;
Sahlol, Ahmed T. .
PLOS ONE, 2020, 15 (06)
[10]  
Farid A.A., 2020, Int. J. Sci. Eng. Res, V11, P1141, DOI DOI 10.14299/IJSER.2020.03.02