Coronavirus, also known as COVID-19, has spread to several countries around the world. It was announced as a pandemic disease by The World Health Organization (WHO) in 2020 for its devastating impact on humans. With the advancements in computer science algorithms, the detection of this type of virus in the early stages is urgently needed for the fast recovery of patients. In this paper, a study of neutrosophic set significance on deep transfer learning models will be presented. The study will be conducted over a limited COVID-19 x-ray. The study relies on neutrosophic set and theory to convert the medical images from the grayscale spatial domain to the neutrosophic domain. The neutrosophic domain consists of three types of images, and they are the True (T) images, the Indeterminacy (I) images, and the Falsity (F) images. The dataset used in this research has been collected from different sources. The dataset is classified into four classes {COVID-19, normal, pneumonia bacterial, and pneumonia virus}. This study aims to review the effect of neutrosophic sets on deep transfer learning models. The selected deep learning models in this study are Alexnet, Googlenet, and Restnet18. Those models are selected as they have a small number of layers on their architectures. To test the performance of the conversion to the neutrosophic domain, more than 36 trials have been conducted and recorded. A combination of training and testing strategies by splitting the dataset into (90–10%, 80–20%, 70–30) is included in the experiments. Four domains of images are tested, and they are, the original domain, the True (T) domain, the Indeterminacy (I) domain, and the Falsity (F) domain. The four domains with the different training and testing strategies were tested using the selected deep transfer models. According to the experimental results, the Indeterminacy (I) neutrosophic domain achieves the highest accuracy possible with 87.1% in the testing accuracy and performance metrics such as Precision, Recall, and F1 Score. The study concludes that using the neutrosophic set with deep learning models may be an encouraging transition to achieve better testing accuracy, especially with limited COVID-19 datasets.

冠状病毒，也称为COVID-19，已经传播到世界各地的多个国家。世界卫生组织（WHO）在2020年宣布它为大流行性疾病，原因是它对人类具有毁灭性影响。随着计算机科学算法的发展，迫切需要在早期阶段检测这种类型的病毒以快速恢复患者。在本文中，将提出中智学对深度迁移学习模型的意义的研究。该研究将通过有限的COVID-19 X射线进行。该研究依靠中智学理论和理论将医学图像从灰度空间域转换为中智学域。中智域包括三种类型的图像，它们是真实（T）图像，不确定性（I）图像和伪造（F）图像。本研究中使用的数据集已从不同来源收集。数据集分为四类{COVID-19，正常，肺炎细菌和肺炎病毒}。这项研究旨在审查中智集对深度迁移学习模型的影响。本研究中选择的深度学习模型是Alexnet，Googlenet和Restnet18。选择这些模型是因为它们的体系结构上只有很少的层。为了测试向中智领域转换的性能，已进行并记录了36多次试验。实验包括将数据集分为（90–10％，80–20％，70–30）的训练和测试策略的组合。测试了图像的四个域，分别是原始域，真实（T）域，不确定（I）域和伪造（F）域。使用选定的深度转移模型对具有不同培训和测试策略的四个域进行了测试。根据实验结果，不确定性（I）的中智领域实现了最高的准确性，测试精度和性能指标（如Precision，Recall和F1得分）达到87.1％。该研究得出的结论是，将中智集与深度学习模型结合使用可能会令人鼓舞，以实现更好的测试准确性，尤其是在有限的COVID-19数据集的情况下。和F1得分。该研究得出的结论是，将中智集与深度学习模型结合使用可能会令人鼓舞，以实现更好的测试准确性，尤其是在有限的COVID-19数据集的情况下。和F1得分。该研究得出的结论是，将中智集与深度学习模型结合使用可能会令人鼓舞，以实现更好的测试准确性，尤其是在有限的COVID-19数据集的情况下。