论文标题
COVID-19使用Staked Ensembles分类：全面分析
COVID-19 Classification Using Staked Ensembles: A Comprehensive Analysis
论文作者
论文摘要
COVID-19的问题随着大量死亡率而增加。这导致了谁宣布这是大流行。在这种情况下，执行高效且快速诊断至关重要。进行逆转录聚合酶链反应（RTPCR）测试以检测SARS-COV-2的存在。该测试是耗时的，而是胸部CT（或胸部X射线）可用于快速准确的诊断。自动诊断被认为很重要，因为它减少了人类的努力并提供准确和低成本的测试。我们的研究的贡献是三倍。首先，它的目的是通过适当的微调程序分析从成立到NAS网络的变异视觉模型的行为和性能。其次，通过绘制单个网络的CAM并用AUCROC曲线确定分类性能，可以在视觉上分析这些模型的行为。第三，对堆叠的合奏技术进行了赋予，以相结合微型模型来提供更高的概括，在这种模型中，通过结合现有的微调网络来设计六个合奏神经网络。暗示这些堆叠的合奏为模型提供了极大的概括。通过组合所有微调网络设计的集合模型获得了99.17％的最新精度得分。 COVID-19类的精度和回忆分别为99.99％和89.79％，类似于堆叠合奏的鲁棒性。
The issue of COVID-19, increasing with a massive mortality rate. This led to the WHO declaring it as a pandemic. In this situation, it is crucial to perform efficient and fast diagnosis. The reverse transcript polymerase chain reaction (RTPCR) test is conducted to detect the presence of SARS-CoV-2. This test is time-consuming and instead chest CT (or Chest X-ray) can be used for a fast and accurate diagnosis. Automated diagnosis is considered to be important as it reduces human effort and provides accurate and low-cost tests. The contributions of our research are three-fold. First, it is aimed to analyse the behaviour and performance of variant vision models ranging from Inception to NAS networks with the appropriate fine-tuning procedure. Second, the behaviour of these models is visually analysed by plotting CAMs for individual networks and determining classification performance with AUCROC curves. Thirdly, stacked ensembles techniques are imparted to provide higher generalisation on combining the fine-tuned models, in which six ensemble neural networks are designed by combining the existing fine-tuned networks. Implying these stacked ensembles provides a great generalization to the models. The ensemble model designed by combining all the fine-tuned networks obtained a state-of-the-art accuracy score of 99.17%. The precision and recall for the COVID-19 class are 99.99% and 89.79% respectively, which resembles the robustness of the stacked ensembles.
