## 4 Datasets
### Classification Datasets  
* **SARS-CoV-2 CT-scan Dataset**[201]. These data have been collected from real patients in hospitals from Sao Paulo, Brazil. The aim of this dataset is to encourage the research and development of artificial intelligent methods that are able to identify if a person is infected by SARS-CoV-2 through the analysis of his/her CT scans. There are 2482 images in total, including gender information;
* **COVID-CT-Dataset**[202]. The COVID-CT-Dataset is a radiologist-confirmed CT image dataset. The images are collected from 760 COVID-19-related preprint PDFs in medRxiv bioRxiv. The labels are decided according to the associated figure captions, while other information, such as age and gender, are also extracted;
* **COVID-CT Dataset**[63]. This dataset contains the full original CT scans of 377 persons, including other information, such as age and sex. It was gathered from Negin radiology located in Sari, Iran, between 5 March and 23 April 2020. There are 15,589 and 48,260 CT scan images belonging to 95 COVID-19 and 282 normal persons, respectively. The format of the exported radiology images was a 16-bit grayscale DICOM format with a 512 \(\times\) 512 pixels resolution;
* **CT Scans for COVID-19 Classification**[203]. Data were collected from two hospitals: Union Hospital (HUST-UH) and Liyuan hospital (HUST-LH). There are a total of 39,370 CT images, and they are in a JPG format with a resolution of 512 \(\times\) 512;
* **Large COVID-19 CT Scan Slice Dataset**[204]. The CT images in this dataset are collected from seven public datasets, which include COVID-CT-Dataset, COVID-CT-MD, Covid-Chestxray-Dataset, MosMedData, COVID-19 CT Lung and Infection Segmentation Dataset, COVID-CTset and COVID-19 CT Segmentation Dataset. There are 17,104 images in total, and all of the CAP images are from the dataset of [PERSON] et al., in which, 25 cases were previously annotated and their radiologist annotated the remaining 35 volumes. The images are in PNG format with a resolution of 512 \(\times\) 512. The dataset also contains information such as gender, age and country;
* **COVIDx Dataset**[155]. The COVIDx Dataset is a combined dataset. The X-ray images in the COVIDx Dataset are collected from more than five different data repositories, which include COVID-19 Image Data Collection, COVID-19 Chest X-ray Dataset Initiative, ActualMed COVID-19 Chest X-ray Dataset Initiative, RSNA Pneumonia Detection Challenge dataset and COVID-19 radiography database; therefore, there are 30,882 images in the dataset. However, the COVIDx Dataset is also a highly imbalanced dataset: the positive samples account for only less than 3% of all samples;
* **COVID-19 Radiography Database**[43]. The COVID-19 Radiography Database is the winner of the COVID-19 Dataset Award of Kaggle. A team of researchers from Qatar University, Doha, Qatar, and the University of Dhaka, Bangladesh, along with their collaborators from Pakistan and Malaysia in collaboration with medical doctors, has created this dataset. The images are mainly collected from the Italian Society of Medical and Interventional Radiology (SIRM) COVID-19 DATABASE and other databases and publications. All of the images are X-ray images, and in PNG format with a resolution of 299 \(\times\) 299;
* **COVID-19 Detection X-ray Dataset**[205]. The 5073 X-ray images are collected from Github user ieeee8023 for COVID-19 X-rays and [PERSON] for Pneumonia Dataset. All images are in JPEG format;
* **Augmented COVID-19 X-ray Images Dataset**[206]. The Augmented COVID-19 X-ray Images Dataset is modified from two datasets, including Covid-Chestxray