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.

翻译：COVID-19问题,随着死亡率的大幅上升而增加。这导致世卫组织将其宣布为流行病。在此情况下,关键是要进行有效和快速的诊断。反转笔录聚合酶链反应(RTPCR)测试是为了检测SARS-COV-2的存在。这项测试耗时费时,而胸部CT(或胸部X光)可以用于快速和准确的诊断。自动诊断被认为很重要,因为它减少了人的努力,提供了准确和低成本的测试。我们的研究贡献有三重。首先,它旨在分析从感知到NAS网络的变异视觉模型的行为和性能,并采用适当的微调程序。第二,这些模型的行为通过为个人网络绘制CAM(或胸部X光)和与AUCROC曲线确定分类性能来进行直观分析。第三,堆叠式组合技术用来对精细调模型进行更高程度的概括,其中六重调神经网络通过将现有的精调网络设计为三重的网络设计成三重度和精确度模型。这些模型的精确度分别用于将99- 199级的堆装精度网络的精确度组合。