SPGC-COVID

SPGC-COVID数据集是由康考迪亚大学信息系统工程研究所开发的，用于区分COVID-19、社区获得性肺炎（CAP）和正常病例的胸部CT扫描数据集。该数据集包含130个案例，涵盖了多种成像设置和不同的医疗中心。数据集的创建旨在通过深度学习框架提高对COVID-19的诊断准确性，并解决训练和测试数据集之间特征差异的问题。此外，数据集还包括了具有心血管疾病或手术史的患者的CT扫描，增加了数据集的复杂性和实用性。

The SPGC-COVID dataset was developed by the Institute of Information Systems Engineering at Concordia University. It is a chest CT scan dataset intended to differentiate COVID-19, community-acquired pneumonia (CAP), and normal clinical cases. The dataset comprises 130 cases spanning multiple imaging protocols and various medical centers. Its development aims to enhance the diagnostic accuracy of COVID-19 using deep learning frameworks, and to resolve the problem of feature distribution discrepancies between training and test datasets. Furthermore, the dataset includes CT scans of patients with a history of cardiovascular disease or surgery, which boosts the complexity and practical applicability of the dataset.