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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 bfyesi sc-jCNfQM igJSrG"><h3>Context</h3>
+<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">Chest X-Ray Dataset</h2></div></div></div><div class="sc-fHzVOS cUYeeo"><div class="sc-davvxH nUNNB"><div style="min-height: 80px;"><div class="sc-etVRix bfyesi sc-jCNfQM igJSrG"><h3>Context</h3>
 <p>This dataset was used to perform the Pneumonia detection using Convolutional Neural Network.</p>
 <h3>Content</h3>
 <p>This dataset mainly consists of the chest X-ray images of Normal and Pneumonia affected patients. There is a total of 5840 chest X-ray images. It has two folders named train and test. Each of them has two sub-folders labeled as NORMAL and PNEUMONIA. This dataset can be used to detect pneumonia by training a convolutional neural network. A sample of the dataset content is given below:</p>