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+<div class="sc-kdrUpr eZtUed"><div class="sc-UEtKG dGqiYy sc-hDzlxo bEIZRR"><div class="sc-fqwslf gsqkEc"><div class="sc-cBQMlg kAHhUk"><h2 class="sc-dcKlJK sc-cVttbi gqEuPW ksnHgj">About Dataset</h2></div></div></div><div class="sc-fHzVOS cUYeeo"><div class="sc-davvxH nUNNB"><div style="min-height: 80px;"><div class="sc-etVRix jqYJaa sc-jCNfQM igJSrG"><h1>COVID-19 X-ray images</h1>
+<h2>About</h2>
+<p>This dataset is a database of COVID-19 cases with chest X-ray or CT images. It contains COVID-19 cases as well as <a rel="noreferrer nofollow" aria-label="MERS (opens in a new tab)" target="_blank" href="https://en.wikipedia.org/wiki/Middle_East_respiratory_syndrome">MERS</a>, <a rel="noreferrer nofollow" aria-label="SARS (opens in a new tab)" target="_blank" href="https://en.wikipedia.org/wiki/Severe_acute_respiratory_syndrome">SARS</a>, and <a rel="noreferrer nofollow" aria-label="ARDS (opens in a new tab)" target="_blank" href="https://en.wikipedia.org/wiki/Acute_respiratory_distress_syndrome">ARDS</a>.</p>
+<h2>Background</h2>
+<p>COVID is possibly better diagnosed using radiological imaging <a rel="noreferrer nofollow" aria-label="Fang, 2020 (opens in a new tab)" target="_blank" href="https://pubs.rsna.org/doi/10.1148/radiol.2020200432">Fang, 2020</a>. Companies are developing AI tools and deploying them at hospitals <a rel="noreferrer nofollow" aria-label="Wired 2020 (opens in a new tab)" target="_blank" href="https://www.wired.com/story/chinese-hospitals-deploy-ai-help-diagnose-covid-19/">Wired 2020</a>. We should have an open database to develop free tools that will also provide assistance.</p>
+<h2>Contribute</h2>
+<p>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 - <a rel="noreferrer nofollow" aria-label="covid-chestxray-dataset (opens in a new tab)" target="_blank" href="https://github.com/ieee8023/covid-chestxray-dataset">covid-chestxray-dataset</a>.</p>
+<h2>Metadata</h2>
+<p>Here is a list of each metadata field, with explanations:</p>
+<ul>
+<li>Patientid (internal identifier, just for this dataset)</li>
+<li>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.)</li>
+<li>sex (M, F, or blank)</li>
+<li>age (age of the patient in years)</li>
+<li>finding (which pneumonia)</li>
+<li>survival (did they survive? Y or N)</li>
+<li>view (for example, PA, AP, or L for X-rays and Axial or Coronal for CT scans)</li>
+<li>modality (CT, X-ray, or something else)</li>
+<li>date (date the image was acquired)</li>
+<li>location (hospital name, city, state, country) importance from right to left.</li>
+<li>filename</li>
+<li>doi (<a rel="noreferrer nofollow" aria-label="DOI (opens in a new tab)" target="_blank" href="https://en.wikipedia.org/wiki/Digital_object_identifier">DOI</a> of the research article</li>
+<li>url (URL of the paper or website where the image came from)</li>
+<li>license</li>
+<li>clinical notes (about the radiograph in particular, not just the patient)</li>
+<li>other notes (e.g. credit)</li>
+</ul></div></div></div>
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