COVID-19 chest xray

# COVID-19 X-ray images

## About
This dataset is a database of COVID-19 cases with chest X-ray or CT images. It contains COVID-19 cases as well as [MERS](https://en.wikipedia.org/wiki/Middle_East_respiratory_syndrome), [SARS](https://en.wikipedia.org/wiki/Severe_acute_respiratory_syndrome), and [ARDS](https://en.wikipedia.org/wiki/Acute_respiratory_distress_syndrome).

## Background
COVID is possibly better diagnosed using radiological imaging [Fang, 2020](https://pubs.rsna.org/doi/10.1148/radiol.2020200432). Companies are developing AI tools and deploying them at hospitals [Wired 2020](https://www.wired.com/story/chinese-hospitals-deploy-ai-help-diagnose-covid-19/). We should have an open database to develop free tools that will also provide assistance.

## Contribute
Your help is needed, use these images in Kaggle kernels to develop AI-based approaches to predict and understand COVID-19. To learn more about the dataset visit the GitHub repo - [covid-chestxray-dataset](https://github.com/ieee8023/covid-chestxray-dataset).

## Metadata
Here is a list of each metadata field, with explanations:
- Patientid (internal identifier, just for this dataset)
- offset (number of days since the start of symptoms or hospitalization for each image, this is very important to have when there are multiple images for the same patient to track progression while being imaged. If a report says "after a few days" let's assume 5 days.)
- sex (M, F, or blank)
- age (age of the patient in years)
- finding (which pneumonia)
- survival (did they survive? Y or N)
- view (for example, PA, AP, or L for X-rays and Axial or Coronal for CT scans)
- modality (CT, X-ray, or something else)
- date (date the image was acquired)
- location (hospital name, city, state, country) importance from right to left.
- filename
- doi ([DOI](https://en.wikipedia.org/wiki/Digital_object_identifier) of the research article
- url (URL of the paper or website where the image came from)
- license
- clinical notes (about the radiograph in particular, not just the patient)
- other notes (e.g. credit)

{'About': '本数据集为一套包含胸部X光或CT图像的COVID-19病例数据库。其中不仅包含COVID-19病例，亦涵盖中东呼吸综合征（MERS）、严重急性呼吸综合征（SARS）及急性呼吸窘迫综合征（ARDS）病例。', 'Background': 'COVID-19疾病或许可通过放射影像学进行更准确的诊断[方, 2020](https://pubs.rsna.org/doi/10.1148/radiol.2020200432)。众多企业正在开发人工智能工具并在医院中部署以协助诊断COVID-19[《连线》2020](https://www.wired.com/story/chinese-hospitals-deploy-ai-help-diagnose-covid-19/)。我们应当拥有一个开放的数据库，以开发免费工具，并提供辅助功能。', 'Contribute': '您的协助至关重要，请使用这些图像在Kaggle核中开发基于人工智能的预测和理解COVID-19的方法。欲了解更多关于数据集的信息，请访问GitHub仓库 - [covid-chestxray-dataset](https://github.com/ieee8023/covid-chestxray-dataset)。', 'Metadata': '以下为每个元数据字段的列表及其解释：
- Patientid（内部标识符，仅适用于本数据集）
- offset（自症状开始或住院以来每个图像的天数，当同一患者存在多张图像时，此信息极为重要，可用于追踪图像过程中的病情进展。若报告提及“数日后”，则假定为5天。）
- sex（M，F或空白）
- age（患者年龄，单位为年）
- finding（何种肺炎）
- survival（是否存活？Y或N）
- view（例如，PA、AP或L，用于X光片；Axial或Coronal，用于CT扫描）
- modality（CT、X光或其他）
- date（图像获取日期）
- location（医院名称、城市、州、国家；从右至左的重要性依次递减）
- filename
- doi（[DOI](https://en.wikipedia.org/wiki/Digital_object_identifier)为研究文章的数字对象标识符）
- url（论文或图像来源网站的URL）
- license
- clinical notes（关于放射影像的具体情况，而不仅限于患者本身）
- other notes（例如，致谢）'}