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In the context of the COVID-19 pandemic, although the peak has passed, a few of cases are still being reported, and lessons from this pandemic provided readiness for responding to new diseases in the future. This paper presents SENet models combined with the ResNet50 architecture with the Squeeze-and-Excitation layer (SE-ResNet50) on various CT scan dataset with different data organizations on high-performance computing cloud environments. The datasets with diverse data structures and multiple image classes per sample are used to make more accurate predictions. Experimental results show that the model achieves high accuracy and the ability to detect cases in multiple classes, helping to minimize missed cases. The research not only demonstrates the effectiveness of the model in diagnosing COVID-19 but also opens up a direction for predicting new diseases in the future and improving the quality of healthcare.

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Proposed Deep Learning SE-ResNet50 Model on Cloud Environments for Improvement of Classified COVID-19 Patient Rates

  • 会议论文

  • 发表日期：

    2025年

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  • Quoc Hung Nguyen,
  • Quoc Viet Bui

摘要

In the context of the COVID-19 pandemic, although the peak has passed, a few of cases are still being reported, and lessons from this pandemic provided readiness for responding to new diseases in the future. This paper presents SENet models combined with the ResNet50 architecture with the Squeeze-and-Excitation layer (SE-ResNet50) on various CT scan dataset with different data organizations on high-performance computing cloud environments. The datasets with diverse data structures and multiple image classes per sample are used to make more accurate predictions. Experimental results show that the model achieves high accuracy and the ability to detect cases in multiple classes, helping to minimize missed cases. The research not only demonstrates the effectiveness of the model in diagnosing COVID-19 but also opens up a direction for predicting new diseases in the future and improving the quality of healthcare.