A comprehensive review of COVID-19 detection with machine learning and deep learning techniques.

Publication date: Jun 07, 2023

The first transmission of coronavirus to humans started in Wuhan city of China, took the shape of a pandemic called Corona Virus Disease 2019 (COVID-19), and posed a principal threat to the entire world. The researchers are trying to inculcate artificial intelligence (Machine learning or deep learning models) for the efficient detection of COVID-19. This research explores all the existing machine learning (ML) or deep learning (DL) models, used for COVID-19 detection which may help the researcher to explore in different directions. The main purpose of this review article is to present a compact overview of the application of artificial intelligence to the research experts, helping them to explore the future scopes of improvement. The researchers have used various machine learning, deep learning, and a combination of machine and deep learning models for extracting significant features and classifying various health conditions in COVID-19 patients. For this purpose, the researchers have utilized different image modalities such as CT-Scan, X-Ray, etc. This study has collected over 200 research papers from various repositories like Google Scholar, PubMed, Web of Science, etc. These research papers were passed through various levels of scrutiny and finally, 50 research articles were selected. In those listed articles, the ML / DL models showed an accuracy of 99% and above while performing the classification of COVID-19. This study has also presented various clinical applications of various research. This study specifies the importance of various machine and deep learning models in the field of medical diagnosis and research. In conclusion, it is evident that ML/DL models have made significant progress in recent years, but there are still limitations that need to be addressed. Overfitting is one such limitation that can lead to incorrect predictions and overburdening of the models. The research community must continue to work towards finding ways to overcome these limitations and make machine and deep learning models even more effective and efficient. Through this ongoing research and development, we can expect even greater advances in the future.

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Concepts Keywords
China COVID-19
Coronavirus CT-Scan
Ct Deep learning
Efficient Machine learning
Google SARS-CoV-2


Type Source Name
disease MESH COVID-19
disease MESH Virus Disease
disease VO efficient
disease VO effective
disease VO virus protein
disease IDO host
disease VO organ
disease MESH death
disease MESH pneumonia
drug DRUGBANK Coenzyme M
disease VO organization
disease VO time
disease IDO country
disease VO USA
drug DRUGBANK Medical air
disease IDO process
disease MESH bacterial pneumonia
drug DRUGBANK Flunarizine
disease MESH pulmonary diseases
disease IDO object
disease IDO algorithm
disease VO frequency
drug DRUGBANK Pixantrone
disease MESH infection
drug DRUGBANK Tropicamide
disease VO efficiency
disease VO Gap
disease VO Severe acute respiratory syndrome coronavirus 2
disease MESH Severe acute respiratory syndrome
disease VO vaccination
disease MESH influenza
drug DRUGBANK (S)-Des-Me-Ampa
drug DRUGBANK S-Arsonocysteine
drug DRUGBANK Guanosine
drug DRUGBANK Carboxyamidotriazole
pathway KEGG Coronavirus disease
disease MESH Viral Pneumonia
drug DRUGBANK Vorinostat
drug DRUGBANK Lauric Acid
disease MESH interstitial lung diseases
disease MESH uncertainty
drug DRUGBANK D-Alanine
disease IDO cell

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