<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 flNyFK"><div style="min-height: 80px;"><div class="sc-etVRix jqYJaa sc-jCNfQM igJSrG"><h3>Context</h3>

<blockquote>

  <p>Pediatric chest X-rays are harder to properly acquire and standardize when compared to adults, as for a children in a dark room, with people watching them from a glass window with strange machinery doesn't make for a comfortable experience.<br>

  At the same time, children present a different physiology that is important to be captured in X-ray classification algorithms, as most datasets tend to focus on adults only.</p>

</blockquote>

<h3>Content</h3>

<blockquote>

  <p>5,856 Chest X-rays labelled as either pneumonia or normal.</p>

</blockquote>

<h3>Acknowledgements</h3>

<p>All credits are due to the authors of the dataset:</p>

<blockquote>

  <p>Kermany D, Goldbaum M, Cai W et al. Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Cell. 2018; 172(5):1122-1131. doi:10.1016/j.cell.2018.02.010</p>

</blockquote>

<p>How to cite this dataset:</p>

<blockquote>

  <p>Kermany, Daniel; Zhang, Kang; Goldbaum, Michael (2018), “Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification”, Mendeley Data, v2<br>

  <a rel="noreferrer nofollow" aria-label="http://dx.doi.org/10.17632/rscbjbr9sj.2 (opens in a new tab)" target="_blank" href="http://dx.doi.org/10.17632/rscbjbr9sj.2">http://dx.doi.org/10.17632/rscbjbr9sj.2</a></p>

</blockquote></div></div></div>