Application of Deep Learning Techniques for COVID-19 Management

The sudden penetration of coronavirus has taken several countries in a devastating state worldwide. The novelty of the COVID-19 virus and its wide range of symptoms have posed significant difficulties for the governments, authorities, and the medical community in dealing with this global outbreak. Various researchers have been focusing their studies on finding methods for this pandemic’s forecasting and diagnosis effectively. Evolutionary advancements in Artificial Intelligence, specifically in Deep Learning, have enabled it to become one of the most promising technologies for solving problems related to the current pandemic and other epidemics in the past. This study presents a Concept Mapping based Research Framework, abbreviated as DL-CoTAUM, to review the usage of Deep Learning Techniques for the management of the COVID-19 pandemic using multiple steps. Through the first step, different concepts around Deep Learning are presented. Multiple deep learning techniques using the basic concepts are studied through the second step. In the next step, various epidemics from the past are learned from the Deep Learning perspective. In the fourth step, multiple use-cases for COVID-19 management are covered up, like trend forecasting, virus and drug discovery, patient diagnosis, clinical analysis, and monitoring for maintaining social distancing & face masks. The last step presents the various evaluation parameters available for analyzing the modeling techniques’ performance under Deep Learning. In this review study, it was found that Deep Learning is gaining importance for various applications under COVID-19 management. Techniques like LSTM, CNN, RNN, and GANs were found to be prominently used modeling techniques for growth curve fitting and image analysis. Accuracies of more than 90% have been reported for Deep Learning techniques in COVID-19 use-cases, which is higher than the other baseline methods. This study will be useful for researchers, academicians, and practitioners in Epidemiological Modeling and Image Processing. The proposed research framework can be used as a reference point by other researchers for studying the application of a new concept in a different domain. And this research study will also be useful for naive users in the field of Deep Learning. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

[1]  Ding-Xuan Zhou,et al.  Universality of Deep Convolutional Neural Networks , 2018, Applied and Computational Harmonic Analysis.

[2]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[3]  Vinay Kumar Reddy Chimmula,et al.  Time series forecasting of COVID-19 transmission in Canada using LSTM networks , 2020, Chaos, Solitons & Fractals.

[4]  Zhibiao Feng,et al.  Separation of Antioxidant Peptides from Pepsin Hydrolysate of Whey Protein Isolate by ATPS of EOPO Co-polymer (UCON)/Phosphate , 2017, Scientific Reports.

[5]  Neena Aloysius,et al.  A review on deep convolutional neural networks , 2017, 2017 International Conference on Communication and Signal Processing (ICCSP).

[6]  G. Pazour,et al.  Ror2 signaling regulates Golgi structure and transport through IFT20 for tumor invasiveness , 2017, Scientific Reports.

[7]  Zhiwei Xu,et al.  Monitoring Pertussis Infections Using Internet Search Queries , 2017, Scientific Reports.

[8]  Yoshua Bengio,et al.  Scaling learning algorithms towards AI , 2007 .

[9]  Vijay V. Raghavan,et al.  A novel data-driven model for real-time influenza forecasting , 2017, bioRxiv.

[10]  B. K. Panigrahi,et al.  Prediction and analysis of COVID-19 positive cases using deep learning models: A descriptive case study of India , 2020, Chaos, Solitons & Fractals.

[11]  Benyuan Liu,et al.  Predicting Flu Trends using Twitter data , 2011, 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[12]  Mei-Ling Shyu,et al.  A Survey on Deep Learning , 2018, ACM Comput. Surv..

[13]  Hong Shan,et al.  Evidence for Gastrointestinal Infection of SARS-CoV-2 , 2020, Gastroenterology.

[14]  David A Winkler,et al.  Performance of Deep and Shallow Neural Networks, the Universal Approximation Theorem, Activity Cliffs, and QSAR , 2017, Molecular informatics.

[15]  A. Gielen,et al.  An Introduction to Concept Mapping as a Participatory Public Health Research Method , 2005, Qualitative health research.

[16]  Kuljeet Singh,et al.  Time series forecasting of Covid-19 using deep learning models: India-USA comparative case study , 2020, Chaos, Solitons & Fractals.

[17]  Heshui Shi,et al.  Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study , 2020, The Lancet Infectious Diseases.

[18]  U. Rajendra Acharya,et al.  Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks , 2020, Computers in Biology and Medicine.

[19]  Samir Kumar Bandyopadhyay,et al.  Machine Learning Approach for Confirmation of COVID-19 Cases: Positive, Negative, Death and Release , 2020, medRxiv.

[20]  D. Coyle,et al.  When piloting health services interventions, what predicts real world behaviours? A systematic concept mapping review , 2020, BMC Medical Research Methodology.

[21]  Mohammad Behdad Mohammad Behdad Jamshidi Jamshidi,et al.  Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment , 2020, Ieee Access.

[22]  K. Yuen,et al.  Imaging Profile of the COVID-19 Infection: Radiologic Findings and Literature Review , 2020, Radiology. Cardiothoracic imaging.

[23]  Alexander Wong,et al.  COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images , 2020, Scientific reports.

[24]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[25]  J. Beier,et al.  The impact of variations in temperature on early Plasmodium falciparum development in Anopheles stephensi , 1995, Parasitology.

[26]  Liang Zhao,et al.  SimNest: Social Media Nested Epidemic Simulation via Online Semi-Supervised Deep Learning , 2015, 2015 IEEE International Conference on Data Mining.

[27]  Adel Javanmard,et al.  Theoretical Insights Into the Optimization Landscape of Over-Parameterized Shallow Neural Networks , 2017, IEEE Transactions on Information Theory.

[28]  Jason Cong,et al.  Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks , 2015, FPGA.

[29]  Zdeněk Peroutka,et al.  Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment , 2020, IEEE Access.

[30]  Christian Igel,et al.  An Introduction to Restricted Boltzmann Machines , 2012, CIARP.

[31]  Reinhard Klette,et al.  Early Diagnosis of Alzheimer's Disease Using Deep Learning , 2019, Proceedings of the 2nd International Conference on Control and Computer Vision - ICCCV 2019.

[32]  Shilu Tong,et al.  Disease surveillance based on Internet-based linear models: an Australian case study of previously unmodeled infection diseases , 2016, Scientific reports.

[33]  François Chollet,et al.  Deep Learning with Python , 2017 .

[34]  Charu C. Aggarwal,et al.  Neural Networks and Deep Learning , 2018, Springer International Publishing.

[35]  Sidong Liu,et al.  Early diagnosis of Alzheimer's disease with deep learning , 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).

[36]  Naren Ramakrishnan,et al.  EpiDeep: Exploiting Embeddings for Epidemic Forecasting , 2019, KDD.

[37]  Bonggun Shin,et al.  Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model , 2020, Computational and Structural Biotechnology Journal.

[38]  Haralambos Sarimveis,et al.  Control of processes with multiple steady states using MPC and RBF neural networks , 2011 .

[39]  Yan Bai,et al.  A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis , 2020, European Respiratory Journal.

[40]  Naren Ramakrishnan,et al.  Flu Gone Viral: Syndromic Surveillance of Flu on Twitter Using Temporal Topic Models , 2014, 2014 IEEE International Conference on Data Mining.

[41]  Jeffrey A. Johnsen,et al.  Concept mapping in mental health: uses and adaptations , 2000 .

[42]  Dong Yu,et al.  Deep Learning: Methods and Applications , 2014, Found. Trends Signal Process..

[43]  Mingjie Jiang,et al.  RetinaMask: A Face Mask detector , 2020, ArXiv.

[44]  Taghi M. Khoshgoftaar,et al.  Deep learning applications and challenges in big data analytics , 2015, Journal of Big Data.

[45]  Alex Graves,et al.  Playing Atari with Deep Reinforcement Learning , 2013, ArXiv.

[46]  Lior Rokach,et al.  Introduction to Knowledge Discovery and Data Mining , 2010, Data Mining and Knowledge Discovery Handbook.

[47]  Tomohide Yagyu,et al.  Relationship of Lifestyle Habits with Sleep Quality among Japanese in Their 20s , 2019, Health.

[48]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[49]  Roberto Maroldi,et al.  Cardiac Involvement in a Patient With Coronavirus Disease 2019 (COVID-19). , 2020, JAMA cardiology.

[50]  Simone C. O. Conceição,et al.  Using concept mapping as a tool for conducting research: An analysis of three approaches , 2017 .

[51]  Taghi M. Khoshgoftaar,et al.  A survey on Image Data Augmentation for Deep Learning , 2019, Journal of Big Data.

[52]  Aravind Sesagiri Raamkumar,et al.  Use of Health Belief Model–Based Deep Learning Classifiers for COVID-19 Social Media Content to Examine Public Perceptions of Physical Distancing: Model Development and Case Study , 2020, JMIR Public Health and Surveillance.

[53]  Anidhya Athaiya,et al.  ACTIVATION FUNCTIONS IN NEURAL NETWORKS , 2020, International Journal of Engineering Applied Sciences and Technology.

[54]  Arzucan Özgür,et al.  DeepDTA: deep drug–target binding affinity prediction , 2018, Bioinform..

[55]  Zhendong Niu,et al.  Deep learning techniques for rating prediction: a survey of the state-of-the-art , 2020, Artificial Intelligence Review.

[56]  P. Khong,et al.  COVID-19 pneumonia: what has CT taught us? , 2020, The Lancet Infectious Diseases.

[57]  Fadi Dornaika,et al.  Deep Learning on Chest X-ray Images to Detect and Evaluate Pneumonia Cases at the Era of COVID-19 , 2020, Journal of Medical Systems.

[58]  Ryad Zemouri,et al.  COMPARATIVE STUDY BETWEEN THE TIMED AUTOMATA AND THE RECURRENT RADIAL BASIS FUNCTION FOR DISCRETE EVENT SYSTEM DIAGNOSIS , 2006 .

[59]  S. Tong,et al.  A large temperature fluctuation may trigger an epidemic erythromelalgia outbreak in China , 2015, Scientific Reports.

[60]  Christian Dagenais,et al.  Concept mapping internal validity: A case of misconceived mapping? , 2017, Evaluation and program planning.

[61]  Sandro Skansi,et al.  Modifications and Extensions to a Feed-Forward Neural Network , 2018 .

[62]  Yong Huang,et al.  Dynamic Forecasting of Zika Epidemics Using Google Trends , 2016, bioRxiv.

[63]  Y. Li,et al.  ALeRT-COVID: Attentive Lockdown-awaRe Transfer Learning for Predicting COVID-19 Pandemics in Different Countries , 2020, Journal of Healthcare Informatics Research.

[64]  Aurélien Géron,et al.  Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems , 2017 .

[65]  Meng Joo Er,et al.  Face recognition with radial basis function (RBF) neural networks , 2002, IEEE Trans. Neural Networks.

[66]  C. O. Lo.  Enhancing groundedness in realist grounded theory research. , 2014 .

[67]  Yuedong Yang,et al.  Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images , 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[68]  U. Rajendra Acharya,et al.  Automated detection of COVID-19 cases using deep neural networks with X-ray images , 2020, Computers in Biology and Medicine.

[69]  Vedran Mrzljak,et al.  Modeling the Spread of COVID-19 Infection Using a Multilayer Perceptron , 2020, Comput. Math. Methods Medicine.