{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:11:23Z","timestamp":1760148683394,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T00:00:00Z","timestamp":1685318400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The prompt and accurate identification of the causes of pneumonia is necessary to implement rapid treatment and preventative approaches, reduce the burden of infections, and develop more successful intervention strategies. There has been an increase in the number of new pneumonia cases and diseases known as acute respiratory distress syndrome (ARDS) as a direct consequence of the spread of COVID-19. Chest radiography has evolved to the point that it is now an indispensable diagnostic tool for COVID-19 infection pneumonia in hospitals. To fully exploit the technique, it is crucial to design a computer-aided diagnostic (CAD) system to assist doctors and other medical professionals in establishing an accurate and rapid diagnosis of pneumonia. This article presents a robust hybrid deep convolutional neural network (DCNN) for rapidly identifying three categories (normal, COVID-19 and pneumonia (viral or bacterial)) using X-ray image data sourced from the COVID-QU-Ex dataset. The proposed approach on the test set achieved a rate of 99.25% accuracy, 99.10% Kappa-score, 99.43% AUC, 99.24% F1-score, 99.25% recall, and 99.23% precision, respectively. The outcomes of the experiments demonstrate that the presented hybrid DCNN mechanism for identifying three categories utilising X-ray images is robust and effective.<\/jats:p>","DOI":"10.3390\/info14060310","type":"journal-article","created":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T03:39:36Z","timestamp":1685331576000},"page":"310","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A Robust Hybrid Deep Convolutional Neural Network for COVID-19 Disease Identification from Chest X-ray Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6849-4241","authenticated-orcid":false,"given":"Theodora","family":"Sanida","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Western Macedonia, 50131 Kozani, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1509-7016","authenticated-orcid":false,"given":"Irene-Maria","family":"Tabakis","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Western Macedonia, 50131 Kozani, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria Vasiliki","family":"Sanida","sequence":"additional","affiliation":[{"name":"Department of Digital Systems, University of Piraeus, 18534 Piraeus, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6252-426X","authenticated-orcid":false,"given":"Argyrios","family":"Sideris","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Western Macedonia, 50131 Kozani, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2180-9752","authenticated-orcid":false,"given":"Minas","family":"Dasygenis","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Western Macedonia, 50131 Kozani, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1002\/lary.29982","article-title":"Clinical features of parosmia associated with COVID-19 infection","volume":"132","author":"Lerner","year":"2022","journal-title":"Laryngoscope"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/s00604-022-05167-y","article-title":"A review on corona virus disease 2019 (COVID-19): Current progress, clinical features and bioanalytical diagnostic methods","volume":"189","author":"Mollarasouli","year":"2022","journal-title":"Microchim. Acta"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1007\/s11046-022-00627-8","article-title":"Clinical features and mortality of COVID-19-associated mucormycosis: A systematic review and meta-analysis","volume":"187","author":"Watanabe","year":"2022","journal-title":"Mycopathologia"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Irmici, G., C\u00e8, M., Caloro, E., Khenkina, N., Della Pepa, G., Ascenti, V., Martinenghi, C., Papa, S., Oliva, G., and Cellina, M. (2023). Chest X-ray in Emergency Radiology: What Artificial Intelligence Applications Are Available?. Diagnostics, 13.","DOI":"10.3390\/diagnostics13020216"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"112830","DOI":"10.1016\/j.bios.2020.112830","article-title":"Diagnosis of COVID-19 for controlling the pandemic: A review of the state-of-the-art","volume":"174","author":"Taleghani","year":"2021","journal-title":"Biosens. Bioelectron."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e12966","DOI":"10.1111\/exsy.12966","article-title":"A cost-sensitive deep learning-based meta-classifier for pediatric pneumonia classification using chest X-rays","volume":"39","author":"Ravi","year":"2022","journal-title":"Expert Syst."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Rajaraman, S., Guo, P., Xue, Z., and Antani, S.K. (2022). A Deep Modality-Specific Ensemble for Improving Pneumonia Detection in Chest X-rays. Diagnostics, 12.","DOI":"10.3390\/diagnostics12061442"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Hasan, M.M., Islam, M.U., Sadeq, M.J., Fung, W.K., and Uddin, J. (2023). Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment. Sensors, 23.","DOI":"10.3390\/s23010527"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1409","DOI":"10.1007\/s10462-021-09985-z","article-title":"Artificial intelligence (AI) for medical imaging to combat coronavirus disease (COVID-19): A detailed review with direction for future research","volume":"55","author":"Soomro","year":"2022","journal-title":"Artif. Intell. Rev."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e532","DOI":"10.1016\/S2589-7500(22)00048-6","article-title":"Identifying who has long COVID in the USA: A machine learning approach using N3C data","volume":"4","author":"Pfaff","year":"2022","journal-title":"Lancet Digit. Health"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ahsan, M.M., Luna, S.A., and Siddique, Z. (2022). Machine-learning-based disease diagnosis: A comprehensive review. Healthcare, 10.","DOI":"10.3390\/healthcare10030541"},{"key":"ref_12","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1709","DOI":"10.1109\/TETC.2020.3018312","article-title":"A novel bio-inspired approach for high-performance management in service-oriented networks","volume":"9","author":"Conti","year":"2020","journal-title":"IEEE Trans. Emerg. Top. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Han, X., Hu, Z., Wang, S., and Zhang, Y. (2022). A Survey on Deep Learning in COVID-19 Diagnosis. J. Imaging, 9.","DOI":"10.3390\/jimaging9010001"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ayadi, M., Ksibi, A., Al-Rasheed, A., and Soufiene, B.O. (2022). COVID-AleXception: A Deep Learning Model Based on a Deep Feature Concatenation Approach for the Detection of COVID-19 from Chest X-ray Images. Healthcare, 10.","DOI":"10.3390\/healthcare10102072"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Hafeez, U., Umer, M., Hameed, A., Mustafa, H., Sohaib, A., Nappi, M., and Madni, H.A. (2022). A CNN based coronavirus disease prediction system for chest X-rays. J. Ambient. Intell. Humaniz. Comput., 1\u201315.","DOI":"10.1007\/s12652-022-03775-3"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"105604","DOI":"10.1016\/j.compbiomed.2022.105604","article-title":"A lightweight CNN-based network on COVID-19 detection using X-ray and CT images","volume":"146","author":"Huang","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"105581","DOI":"10.1016\/j.cmpb.2020.105581","article-title":"CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest X-ray images","volume":"196","author":"Khan","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_21","first-page":"200130","article-title":"Deep viewing for the identification of covid-19 infection status from chest X-ray image using cnn based architecture","volume":"16","author":"Ghose","year":"2022","journal-title":"Intell. Syst. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"654","DOI":"10.3390\/biomedinformatics2040043","article-title":"Deep Learning Model for COVID-19-Infected Pneumonia Diagnosis Using Chest Radiography Images","volume":"2","author":"Ibrokhimov","year":"2022","journal-title":"BioMedInformatics"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Khan, I.U., and Aslam, N. (2020). A deep-learning-based framework for automated diagnosis of COVID-19 using X-ray images. Information, 11.","DOI":"10.3390\/info11090419"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5521","DOI":"10.1007\/s00500-022-07798-y","article-title":"A MobileNet-based CNN model with a novel fine-tuning mechanism for COVID-19 infection detection","volume":"27","author":"Kaya","year":"2023","journal-title":"Soft Comput."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Nayak, S.R., Nayak, D.R., Sinha, U., Arora, V., and Pachori, R.B. (2023). An Efficient Deep Learning Method for Detection of COVID-19 Infection Using Chest X-ray Images. Diagnostics, 13.","DOI":"10.3390\/diagnostics13010131"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sanida, T., Sideris, A., Tsiktsiris, D., and Dasygenis, M. (2022). Lightweight neural network for COVID-19 detection from chest X-ray images implemented on an embedded system. Technologies, 10.","DOI":"10.3390\/technologies10020037"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sanida, T., Sideris, A., Chatzisavvas, A., Dossis, M., and Dasygenis, M. (2022, January 23\u201325). Radiography Images with Transfer Learning on Embedded System. Proceedings of the 2022 7th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), Ioannina, Greece.","DOI":"10.1109\/SEEDA-CECNSM57760.2022.9932978"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"105002","DOI":"10.1016\/j.compbiomed.2021.105002","article-title":"COVID-19 infection localization and severity grading from chest X-ray images","volume":"139","author":"Tahir","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1186\/s43055-020-00296-x","article-title":"Chest X-ray findings monitoring COVID-19 disease course and severity","volume":"51","author":"Yasin","year":"2020","journal-title":"Egypt. J. Radiol. Nucl. Med."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Rousan, L.A., Elobeid, E., Karrar, M., and Khader, Y. (2020). Chest X-ray findings and temporal lung changes in patients with COVID-19 pneumonia. BMC Pulm. Med., 20.","DOI":"10.1186\/s12890-020-01286-5"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Sanida, M.V., Sanida, T., Sideris, A., and Dasygenis, M. (2023). An Efficient Hybrid CNN Classification Model for Tomato Crop Disease. Technologies, 11.","DOI":"10.3390\/technologies11010010"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.3389\/fpls.2016.01419","article-title":"Using deep learning for image-based plant disease detection","volume":"7","author":"Mohanty","year":"2016","journal-title":"Front. Plant Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"15041","DOI":"10.1007\/s11042-022-12461-7","article-title":"A heterogeneous implementation for plant disease identification using deep learning","volume":"81","author":"Sanida","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.aci.2018.08.003","article-title":"Classification assessment methods","volume":"17","author":"Tharwat","year":"2020","journal-title":"Appl. Comput. Inform."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Delgado, R., and Tibau, X.A. (2019). Why Cohen\u2019s Kappa should be avoided as performance measure in classification. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0222916"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/14\/6\/310\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:44:06Z","timestamp":1760125446000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/14\/6\/310"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,29]]},"references-count":36,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["info14060310"],"URL":"https:\/\/doi.org\/10.3390\/info14060310","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2023,5,29]]}}}