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Deep learning techniques significantly enhance the diagnosis and forecasting of COVID-19 through medical imaging, providing faster and more accurate results than traditional methods. The pandemic highlighted the need for rapid diagnostic methods, leading to the integration of imaging modalities like chest X-rays and CT scans with RT-PCR tests. This review emphasizes how artificial intelligence, particularly deep learning, is revolutionizing COVID-19 diagnosis by autonomously extracting and classifying features from medical images.
A systematic review of 400 studies from 2020 to 2023 reveals that deep learning models consistently outperform traditional machine learning in diagnostic accuracy. For example, studies utilizing chest X-rays achieved over 90% accuracy in detecting COVID-19, showcasing the effectiveness of computer-aided diagnostic systems. Despite the promising advancements, challenges such as data variability and model explainability remain critical, necessitating further research to refine these technologies for future public health crises.
• Deep learning models enhance COVID-19 diagnosis accuracy using medical imaging.
• Studies show over 90% accuracy in detecting COVID-19 with chest X-rays.
Deep learning models autonomously extract and classify features from medical images, improving diagnostic precision.
CADS utilize deep learning to enhance the accuracy of COVID-19 detection through imaging.
Predictive modelling in deep learning aids in forecasting COVID-19 outcomes based on imaging data.
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