{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T17:54:14Z","timestamp":1790186054099,"version":"4.1.0"},"reference-count":135,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,3,7]],"date-time":"2022-03-07T00:00:00Z","timestamp":1646611200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background and Motivation: The novel coronavirus causing COVID-19 is exceptionally contagious, highly mutative, decimating human health and life, as well as the global economy, by consistent evolution of new pernicious variants and outbreaks. The reverse transcriptase polymerase chain reaction currently used for diagnosis has major limitations. Furthermore, the multiclass lung classification X-ray systems having viral, bacterial, and tubercular classes\u2014including COVID-19\u2014are not reliable. Thus, there is a need for a robust, fast, cost-effective, and easily available diagnostic method. Method: Artificial intelligence (AI) has been shown to revolutionize all walks of life, particularly medical imaging. This study proposes a deep learning AI-based automatic multiclass detection and classification of pneumonia from chest X-ray images that are readily available and highly cost-effective. The study has designed and applied seven highly efficient pre-trained convolutional neural networks\u2014namely, VGG16, VGG19, DenseNet201, Xception, InceptionV3, NasnetMobile, and ResNet152\u2014for classification of up to five classes of pneumonia. Results: The database consisted of 18,603 scans with two, three, and five classes. The best results were using DenseNet201, VGG16, and VGG16, respectively having accuracies of 99.84%, 96.7%, 92.67%; sensitivity of 99.84%, 96.63%, 92.70%; specificity of 99.84, 96.63%, 92.41%; and AUC of 1.0, 0.97, 0.92 (p &lt; 0.0001 for all), respectively. Our system outperformed existing methods by 1.2% for the five-class model. The online system takes &lt;1 s while demonstrating reliability and stability. Conclusions: Deep learning AI is a powerful paradigm for multiclass pneumonia classification.<\/jats:p>","DOI":"10.3390\/diagnostics12030652","type":"journal-article","created":{"date-parts":[[2022,3,7]],"date-time":"2022-03-07T10:21:16Z","timestamp":1646648476000},"page":"652","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["Four Types of Multiclass Frameworks for Pneumonia Classification and Its Validation in X-ray Scans Using Seven Types of Deep Learning Artificial Intelligence Models"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0502-4816","authenticated-orcid":false,"family":"Nillmani","sequence":"first","affiliation":[{"name":"School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi 221005, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4600-8513","authenticated-orcid":false,"given":"Pankaj","family":"Jain","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi 221005, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neeraj","family":"Sharma","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi 221005, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mannudeep","family":"Kalra","sequence":"additional","affiliation":[{"name":"Department of Radiology, Massachusetts General Hospital, Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5927-3201","authenticated-orcid":false,"given":"Klaudija","family":"Viskovic","sequence":"additional","affiliation":[{"name":"Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, 10000 Zagreb, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luca","family":"Saba","sequence":"additional","affiliation":[{"name":"Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 10015 Cagliari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jasjit","family":"Suri","sequence":"additional","affiliation":[{"name":"Stroke Diagnostic and Monitoring Division, AtheroPoint<sup>TM<\/sup>, Roseville, CA 95661, USA"},{"name":"Knowledge Engineering Center, Global Biomedical Technologies, Inc., Roseville, CA 95661, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1111\/cns.13372","article-title":"Neurological manifestations in COVID-19 caused by SARS-CoV-2","volume":"26","author":"Baig","year":"2020","journal-title":"CNS Neurosci. 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