{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T18:44:26Z","timestamp":1767984266250,"version":"3.49.0"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"41","license":[{"start":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T00:00:00Z","timestamp":1711411200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T00:00:00Z","timestamp":1711411200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Alma Mater Studiorum - Universit\u00e0 di Bologna"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In recent years, the COVID-19 outbreak has affected humanity across the globe. The frequent symptoms of COVID-19 are identical to the normal flu, such as fever and cough. COVID-19 disseminates rapidly, and it has become a prominent cause of mortality. Nowadays, the new wave of COVID-19 has created significant impacts in China. This virus can have detrimental effects on people of all ages, particularly the elderly, due to their weak immune systems. The real-time polymerase chain reaction (RT-PCR) examination is typically performed for the identification of coronavirus. RT-PCR is an expensive and time requiring method, accompanied by a significant rate of false negative detections. Therefore, it is mandatory to develop an inexpensive, fast, and reliable method to detect COVID-19. X-ray images are generally utilized to detect diverse respiratory conditions like pulmonary infections, breathlessness syndrome, lung cancer, air collection in spaces of the lungs, etc. This study has also utilized a chest X-ray dataset to identify COVID-19 and pneumonia. In this research work, we proposed a novel deep learning model CP_DeepNet, which is based on a pre-trained deep learning model such as SqueezeNet, and further added three blocks of convolutional layers to it for assessing the classification efficacy. Furthermore, we employed a data augmentation method for generating more images to overcome the problem of model overfitting. We utilized COVID-19 radiograph dataset for evaluating the performance of the proposed model. To elaborate further, we obtained significant results with accuracy of 99.32%, a precision of 100%, a recall of 99%, a specificity of 99.2%, an area under the curve of 99.78%, and an F1-score of 99.49% on CP_DeepNet for the binary classification of COVID-19 and normal class. We also employed CP_DeepNet for the multiclass classification of COVID-19, pneumonia, and normal person, in which CP_DeepNet achieved accuracy, precision, recall, specificity, area under curve, and F1-score of 99.62%, 99.79%, 99.52%, 99.69, 99.62, and 99.72%, respectively. Comparative analysis of experimental results with different preexisting techniques shows that the proposed model is more dependable as compared to RT-PCR and other prevailing modern techniques for the detection of COVID-19.<\/jats:p>","DOI":"10.1007\/s11042-024-18921-6","type":"journal-article","created":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T06:19:47Z","timestamp":1711433987000},"page":"88681-88698","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["CP_DeepNet: a novel automated system for COVID-19 and pneumonia detection through lung X-rays"],"prefix":"10.1007","volume":"83","author":[{"given":"Muhammad Hamza","family":"Mehmood","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5406-9201","authenticated-orcid":false,"given":"Farman","family":"Hassan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Auliya Ur","family":"Rahman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wasiat","family":"Khan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samih M.","family":"Mostafa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yazeed Yasin","family":"Ghadi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdulmohsen","family":"Algarni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mudasser","family":"Ali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,3,26]]},"reference":[{"key":"18921_CR1","unstructured":"Gabriel Wildau (2023) China: Covid wave sparks chaos but not worst-case scenarios. Teneo. Retrieved January 4, 2023, from https:\/\/www.teneo.com\/china-covid-wave-sparks-chaos-but-not-worst-case-scenarios\/. Accessed 4 July 2023"},{"key":"18921_CR2","unstructured":"Worldometers (2022) Coronavirus cases. Available: https:\/\/www.worldometers.info\/coronavirus\/. Accessed 4 July 2023"},{"key":"18921_CR3","unstructured":"COVID-19 Gov PK - apps on Google Play (2022) Google. Available: https:\/\/play.google.com\/store\/apps\/details?id=com.govpk.covid19&hl=en_US&gl=US. Accessed 4 July 2023"},{"key":"18921_CR4","unstructured":"Bahl R (2020) Here's how fast COVID-19 can spread in a household. Healthline. Healthline Media. Available at: https:\/\/www.healthline.com\/health-news\/how-fast-covid-19-can-spread-in-a-household. Accessed 9 July 2023"},{"key":"18921_CR5","unstructured":"How coronavirus is transmitted: Here are all the ways it can spread (2022) WebMD. WebMD. Available at: https:\/\/www.webmd.com\/covid\/coronavirus-transmission-overview. Accessed 4 July 2023"},{"key":"18921_CR6","doi-asserted-by":"crossref","unstructured":"Ai T, Yang Z, Hou H, Zhan C, Chen C, Lv W, ... Xia L (2020) Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology 296(2):E32-E40","DOI":"10.1148\/radiol.2020200642"},{"key":"18921_CR7","doi-asserted-by":"crossref","unstructured":"Narin A, Kaya C, Pamuk Z (2021) Automatic detection of coronavirus disease (COVID-19) using x-ray images and deep convolutional neural networks. Pattern Anal Appl 24(3):1207-1220","DOI":"10.1007\/s10044-021-00984-y"},{"key":"18921_CR8","doi-asserted-by":"crossref","unstructured":"Maghdid HS, Asaad AT, Ghafoor KZ, Sadiq AS, Mirjalili S, Khan MK (2021) Diagnosing COVID-19 pneumonia from X-ray and CT images using deep learning and transfer learning algorithms. In Multimodal Image Exploitation and Learning 2021 (Vol. 11734, p. 117340E). International Society for Optics and Photonics","DOI":"10.1117\/12.2588672"},{"key":"18921_CR9","doi-asserted-by":"crossref","unstructured":"Bukhari SUK, Bukhari SSK, Syed A, Shah SSH (2020) The diagnostic evaluation of Convolutional Neural Network (CNN) for the assessment of chest X-ray of patients infected with COVID-19. MedRxiv 2020\u20132003","DOI":"10.1101\/2020.03.26.20044610"},{"key":"18921_CR10","unstructured":"Rachna C (2020) Difference between X-ray and CT scan. Rahman, T., Chowdhury, M., & Khandakar, A.(2020). COVID-19 radiography database. Kaggle. Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C., & Shpanskaya, K.(2017). Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. 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