{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T10:15:13Z","timestamp":1784542513239,"version":"3.55.0"},"reference-count":31,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2024,7,8]],"date-time":"2024-07-08T00:00:00Z","timestamp":1720396800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>During the COVID-19 pandemic, pneumonia was the leading cause of respiratory failure and death. In addition to SARS-COV-2, it can be caused by several other bacterial and viral agents. Even today, variants of SARS-COV-2 are endemic and COVID-19 cases are common in many places. The symptoms of COVID-19 are highly diverse and robust, ranging from invisible to severe respiratory failure. Current detection methods for the disease are time-consuming and expensive with low accuracy and precision. To address such situations, we have designed a framework for COVID-19 and Pneumonia detection using multiple deep learning algorithms further accompanied by a deployment scheme. In this study, we have utilized four prominent deep learning models, which are VGG-19, ResNet-50, Inception V3 and Xception, on two separate datasets of CT scan and X-ray images (COVID\/Non-COVID) to identify the best models for the detection of COVID-19. We achieved accuracies ranging from 86% to 99% depending on the model and dataset. To further validate our findings, we have applied the four distinct models on two more supplementary datasets of X-ray images of bacterial pneumonia and viral pneumonia. Additionally, we have implemented a flask app to visualize the outcome of our framework to show the identified COVID and Non-COVID images. The findings of this study will be helpful to develop an AI-driven automated tool for the cost effective and faster detection and better management of COVID-19 patients.<\/jats:p>","DOI":"10.1371\/journal.pone.0302413","type":"journal-article","created":{"date-parts":[[2024,7,8]],"date-time":"2024-07-08T18:57:01Z","timestamp":1720465021000},"page":"e0302413","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":17,"title":["COVID-19 and Pneumonia detection and web deployment from CT scan and X-ray images using deep learning"],"prefix":"10.1371","volume":"19","author":[{"given":"Nahid","family":"Islam","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9281-9088","authenticated-orcid":true,"given":"Abu S. 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Adnan","family":"Islam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Omar","family":"Sadat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nahid Hossain","family":"Taz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,7,8]]},"reference":[{"key":"pone.0302413.ref001","unstructured":"who.org, COVID-19 cases | WHO COVID-19 dashboard,\u201d datadot."},{"issue":"9","key":"pone.0302413.ref002","article-title":"COVID-19 infection: Origin, transmission, and characteristics of human coronaviruses","volume":"24","author":"M.A. Shereen","year":"2020","journal-title":"Journal of advanced research"},{"key":"pone.0302413.ref003","year":"2024","journal-title":"COVID-19 epidemiological update\u2013 19 January 2024"},{"issue":"5","key":"pone.0302413.ref004","doi-asserted-by":"crossref","first-page":"e7","DOI":"10.1016\/j.jinf.2020.03.007","article-title":"Clinical and CT imaging features of the COVID-19 pneumonia: Focus on pregnant women and children","volume":"80","author":"H. 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