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COVID-19 is a unique and devastating respiratory disease outbreak that has affected global populations as the disease spreads rapidly. Many deep learning breakthroughs may improve COVID-19 prediction and forecasting as a tool for precise and fast detection. In this study, the dataset used contained 8055 CT image samples, 5427 of which were COVID cases and 2628 non-COVID. Again, 9544 X-ray samples included 4044 COVID patients and 5500 non-COVID cases. MobileNetV3, DenseNet201, and GoogleNet InceptionV1 show the highest accuracy of 97.872%, 97.567%, and 97.643%, respectively. The high accuracy indicates that these models can make many accurate predictions, as well as others, are also high for MobileNetV3 and DenseNet201. An extensive evaluation using accuracy, precision, and recall allows a comprehensive comparison to improve predictive models by combining loss optimization with scalable batch normalization. This research shows that these tactics improve model performance and resilience for advancing COVID-19 prediction and detection and show how deep learning can improve disease handling. The methods suggested in this research would strengthen healthcare systems, policymakers, and researchers to make educated decisions to reduce COVID-19 and other contagious diseases.

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Unleashing Modified Deep Learning Models in Efficient COVID-19 Detection

  • 会议论文

  • 发表日期：

    2025年

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  • Md. Aminul Islam,
  • Shabbir Ahmed Shuvo,
  • Mohammad Abu Tareq Rony,
  • M. Raihan,
  • Md. Abu Sufian

摘要

COVID-19 is a unique and devastating respiratory disease outbreak that has affected global populations as the disease spreads rapidly. Many deep learning breakthroughs may improve COVID-19 prediction and forecasting as a tool for precise and fast detection. In this study, the dataset used contained 8055 CT image samples, 5427 of which were COVID cases and 2628 non-COVID. Again, 9544 X-ray samples included 4044 COVID patients and 5500 non-COVID cases. MobileNetV3, DenseNet201, and GoogleNet InceptionV1 show the highest accuracy of 97.872%, 97.567%, and 97.643%, respectively. The high accuracy indicates that these models can make many accurate predictions, as well as others, are also high for MobileNetV3 and DenseNet201. An extensive evaluation using accuracy, precision, and recall allows a comprehensive comparison to improve predictive models by combining loss optimization with scalable batch normalization. This research shows that these tactics improve model performance and resilience for advancing COVID-19 prediction and detection and show how deep learning can improve disease handling. The methods suggested in this research would strengthen healthcare systems, policymakers, and researchers to make educated decisions to reduce COVID-19 and other contagious diseases.