{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T20:27:26Z","timestamp":1770236846145,"version":"3.49.0"},"reference-count":37,"publisher":"China Science Publishing & Media Ltd.","issue":"4","license":[{"start":{"date-parts":[[2023,12,20]],"date-time":"2023-12-20T00:00:00Z","timestamp":1703030400000},"content-version":"vor","delay-in-days":353,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,11,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n               <jats:p>Lung infiltration is a non-communicable condition where materials with higher density than air exist in the parenchyma tissue of the lungs. Lung infiltration can be hard to be detected in an X-ray scan even for a radiologist, especially at the early stages making it a leading cause of death. In response, several deep learning approaches have been evolved to address this problem. This paper proposes the Slide-Detect technique which is a Deep Neural Networks (DNN) model based on Convolutional Neural Networks (CNNs) that is trained to diagnose lung infiltration with Area Under Curve (AUC) up to 91.47%, accuracy of 93.85% and relatively low computational resources.<\/jats:p>","DOI":"10.1162\/dint_a_00233","type":"journal-article","created":{"date-parts":[[2023,10,20]],"date-time":"2023-10-20T14:00:31Z","timestamp":1697810431000},"page":"1048-1062","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":6,"title":["Slide-Detect: An Accurate Deep Learning Diagnosis of Lung Infiltration"],"prefix":"10.3724","volume":"5","author":[{"given":"Ahmed E.","family":"Mohamed","sequence":"first","affiliation":[]},{"given":"Magda 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