{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:23:22Z","timestamp":1760232202894,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T00:00:00Z","timestamp":1666137600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"the Basic Science Research Program through the National Research Foundation of Korea (NRF)","doi-asserted-by":"publisher","award":["2021R1A1A03040177"],"award-info":[{"award-number":["2021R1A1A03040177"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>COVID-19 has infected millions of people worldwide over the past few years. The main technique used for COVID-19 detection is reverse transcription, which is expensive, sensitive, and requires medical expertise. X-ray imaging is an alternative and more accessible technique. This study aimed to improve detection accuracy to create a computer-aided diagnostic tool. Combining other artificial intelligence applications techniques with radiological imaging can help detect different diseases. This study proposes a technique for the automatic detection of COVID-19 and other chest-related diseases using digital chest X-ray images of suspected patients by applying transfer learning (TL) algorithms. For this purpose, two balanced datasets, Dataset-1 and Dataset-2, were created by combining four public databases and collecting images from recently published articles. Dataset-1 consisted of 6000 chest X-ray images with 1500 for each class. Dataset-2 consisted of 7200 images with 1200 for each class. To train and test the model, TL with nine pretrained convolutional neural networks (CNNs) was used with augmentation as a preprocessing method. The network was trained to classify using five classifiers: two-class classifier (normal and COVID-19); three-class classifier (normal, COVID-19, and viral pneumonia), four-class classifier (normal, viral pneumonia, COVID-19, and tuberculosis (Tb)), five-class classifier (normal, bacterial pneumonia, COVID-19, Tb, and pneumothorax), and six-class classifier (normal, bacterial pneumonia, COVID-19, viral pneumonia, Tb, and pneumothorax). For two, three, four, five, and six classes, our model achieved a maximum accuracy of 99.83, 98.11, 97.00, 94.66, and 87.29%, respectively.<\/jats:p>","DOI":"10.3390\/s22207977","type":"journal-article","created":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T22:19:53Z","timestamp":1666217993000},"page":"7977","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Classification and Detection of COVID-19 and Other Chest-Related Diseases Using Transfer Learning"],"prefix":"10.3390","volume":"22","author":[{"given":"Muhammad Tahir","family":"Naseem","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, Yeungnam University, Gyeongsan 38541, Korea"},{"name":"Riphah School of Computing & Applied Sciences (RSCI), Riphah International University, Lahore 55150, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tajmal","family":"Hussain","sequence":"additional","affiliation":[{"name":"Riphah School of Computing & Applied Sciences (RSCI), Riphah International University, Lahore 55150, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9606-0646","authenticated-orcid":false,"given":"Chan-Su","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Yeungnam University, Gyeongsan 38541, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4854-9935","authenticated-orcid":false,"given":"Muhammad Adnan","family":"Khan","sequence":"additional","affiliation":[{"name":"Riphah School of Computing & Applied Sciences (RSCI), Riphah International University, Lahore 55150, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102433","DOI":"10.1016\/j.jaut.2020.102433","article-title":"The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak","volume":"109","author":"Rothan","year":"2020","journal-title":"J. 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