Literature DB >> 33513984

Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions.

Tarik Alafif1, Abdul Muneeim Tehame2, Saleh Bajaba3, Ahmed Barnawi4, Saad Zia5.   

Abstract

With many successful stories, machine learning (ML) and deep learning (DL) have been widely used in our everyday lives in a number of ways. They have also been instrumental in tackling the outbreak of Coronavirus (COVID-19), which has been happening around the world. The SARS-CoV-2 virus-induced COVID-19 epidemic has spread rapidly across the world, leading to international outbreaks. The COVID-19 fight to curb the spread of the disease involves most states, companies, and scientific research institutions. In this research, we look at the Artificial Intelligence (AI)-based ML and DL methods for COVID-19 diagnosis and treatment. Furthermore, in the battle against COVID-19, we summarize the AI-based ML and DL methods and the available datasets, tools, and performance. This survey offers a detailed overview of the existing state-of-the-art methodologies for ML and DL researchers and the wider health community with descriptions of how ML and DL and data can improve the status of COVID-19, and more studies in order to avoid the outbreak of COVID-19. Details of challenges and future directions are also provided.

Entities:  

Keywords:  COVID-19; artificial intelligence; deep learning; diagnosis; machine learning; treatment

Year:  2021        PMID: 33513984     DOI: 10.3390/ijerph18031117

Source DB:  PubMed          Journal:  Int J Environ Res Public Health        ISSN: 1660-4601            Impact factor:   3.390


  21 in total

1.  DISCOVID: discovering patterns of COVID-19 infection from recovered patients: a case study in Saudi Arabia.

Authors:  Tarik Alafif; Alaa Etaiwi; Yousef Hawsawi; Abdulmajeed Alrefaei; Ayman Albassam; Hassan Althobaiti
Journal:  Int J Inf Technol       Date:  2022-07-04

2.  Development of Machine-Learning Model to Predict COVID-19 Mortality: Application of Ensemble Model and Regarding Feature Impacts.

Authors:  Seung-Min Baik; Miae Lee; Kyung-Sook Hong; Dong-Jin Park
Journal:  Diagnostics (Basel)       Date:  2022-06-14

Review 3.  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

4.  A composite ranking of risk factors for COVID-19 time-to-event data from a Turkish cohort.

Authors:  Ayse Ulgen; Sirin Cetin; Meryem Cetin; Hakan Sivgin; Wentian Li
Journal:  Comput Biol Chem       Date:  2022-04-09       Impact factor: 3.737

5.  Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice.

Authors:  Kranthi Kumar Lella; Alphonse Pja
Journal:  AIMS Public Health       Date:  2021-03-10

6.  Abnormality detection and intelligent severity assessment of human chest computed tomography scans using deep learning: a case study on SARS-COV-2 assessment.

Authors:  Mohamed Ramzy Ibrahim; Sherin M Youssef; Karma M Fathalla
Journal:  J Ambient Intell Humaniz Comput       Date:  2021-05-25

7.  An Automated Glowworm Swarm Optimization with an Inception-Based Deep Convolutional Neural Network for COVID-19 Diagnosis and Classification.

Authors:  Ibrahim Abunadi; Amani Abdulrahman Albraikan; Jaber S Alzahrani; Majdy M Eltahir; Anwer Mustafa Hilal; Mohamed I Eldesouki; Abdelwahed Motwakel; Ishfaq Yaseen
Journal:  Healthcare (Basel)       Date:  2022-04-08

8.  Leveraging Artificial Intelligence (AI) Capabilities for COVID-19 Containment.

Authors:  Chellammal Surianarayanan; Pethuru Raj Chelliah
Journal:  New Gener Comput       Date:  2021-06-10       Impact factor: 1.048

9.  A deep-learning based multimodal system for Covid-19 diagnosis using breathing sounds and chest X-ray images.

Authors:  Unais Sait; Gokul Lal K V; Sanjana Shivakumar; Tarun Kumar; Rahul Bhaumik; Sunny Prajapati; Kriti Bhalla; Anaghaa Chakrapani
Journal:  Appl Soft Comput       Date:  2021-05-26       Impact factor: 6.725

10.  HANA: A Healthy Artificial Nutrition Analysis model during COVID-19 pandemic.

Authors:  Mahmoud Y Shams; Omar M Elzeki; Lobna M Abouelmagd; Aboul Ella Hassanien; Mohamed Abd Elfattah; Hanaa Salem
Journal:  Comput Biol Med       Date:  2021-06-30       Impact factor: 4.589

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