Challenges issues and future recommendations of deep learning techniques for SARS-CoV-2 detection utilising X-ray and CT images: a comprehensive review DOI Creative Commons
Md Shofiqul Islam, Fahmid Al Farid, F. M. Javed Mehedi Shamrat

et al.

PeerJ Computer Science, Journal Year: 2024, Volume and Issue: 10, P. e2517 - e2517

Published: Dec. 24, 2024

The global spread of SARS-CoV-2 has prompted a crucial need for accurate medical diagnosis, particularly in the respiratory system. Current diagnostic methods heavily rely on imaging techniques like CT scans and X-rays, but identifying these images proves to be challenging time-consuming. In this context, artificial intelligence (AI) models, specifically deep learning (DL) networks, emerge as promising solution image analysis. This article provides meticulous comprehensive review imaging-based diagnosis using up May 2024. starts with an overview covering basic steps learning-based data sources, pre-processing methods, taxonomy techniques, findings, research gaps performance evaluation. We also focus addressing current privacy issues, limitations, challenges realm diagnosis. According taxonomy, each model is discussed, encompassing its core functionality critical assessment suitability detection. A comparative analysis included by summarizing all relevant studies provide overall visualization. Considering best deep-learning detection, conducts experiment twelve contemporary techniques. experimental result shows that MobileNetV3 outperforms other models accuracy 98.11%. Finally, elaborates explores potential future directions methodological recommendations advancement.

Language: Английский

Challenges issues and future recommendations of deep learning techniques for SARS-CoV-2 detection utilising X-ray and CT images: a comprehensive review DOI Creative Commons
Md Shofiqul Islam, Fahmid Al Farid, F. M. Javed Mehedi Shamrat

et al.

PeerJ Computer Science, Journal Year: 2024, Volume and Issue: 10, P. e2517 - e2517

Published: Dec. 24, 2024

The global spread of SARS-CoV-2 has prompted a crucial need for accurate medical diagnosis, particularly in the respiratory system. Current diagnostic methods heavily rely on imaging techniques like CT scans and X-rays, but identifying these images proves to be challenging time-consuming. In this context, artificial intelligence (AI) models, specifically deep learning (DL) networks, emerge as promising solution image analysis. This article provides meticulous comprehensive review imaging-based diagnosis using up May 2024. starts with an overview covering basic steps learning-based data sources, pre-processing methods, taxonomy techniques, findings, research gaps performance evaluation. We also focus addressing current privacy issues, limitations, challenges realm diagnosis. According taxonomy, each model is discussed, encompassing its core functionality critical assessment suitability detection. A comparative analysis included by summarizing all relevant studies provide overall visualization. Considering best deep-learning detection, conducts experiment twelve contemporary techniques. experimental result shows that MobileNetV3 outperforms other models accuracy 98.11%. Finally, elaborates explores potential future directions methodological recommendations advancement.

Language: Английский

Citations

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