{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T16:54:22Z","timestamp":1781283262689,"version":"3.54.1"},"reference-count":51,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,5,3]],"date-time":"2023-05-03T00:00:00Z","timestamp":1683072000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100008982","name":"Qatar National Research Fund (QNRF)","doi-asserted-by":"publisher","award":["UREP28-144-3-046"],"award-info":[{"award-number":["UREP28-144-3-046"]}],"id":[{"id":"10.13039\/100008982","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008982","name":"Qatar National Research Fund (QNRF)","doi-asserted-by":"publisher","award":["QUST-1-CENG-2023-795"],"award-info":[{"award-number":["QUST-1-CENG-2023-795"]}],"id":[{"id":"10.13039\/100008982","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Qatar University student grant","award":["UREP28-144-3-046"],"award-info":[{"award-number":["UREP28-144-3-046"]}]},{"name":"Qatar University student grant","award":["QUST-1-CENG-2023-795"],"award-info":[{"award-number":["QUST-1-CENG-2023-795"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Rapid identification of COVID-19 can assist in making decisions for effective treatment and epidemic prevention. The PCR-based test is expert-dependent, is time-consuming, and has limited sensitivity. By inspecting Chest R-ray (CXR) images, COVID-19, pneumonia, and other lung infections can be detected in real time. The current, state-of-the-art literature suggests that deep learning (DL) is highly advantageous in automatic disease classification utilizing the CXR images. The goal of this study is to develop models by employing DL models for identifying COVID-19 and other lung disorders more efficiently. For this study, a dataset of 18,564 CXR images with seven disease categories was created from multiple publicly available sources. Four DL architectures including the proposed CNN model and pretrained VGG-16, VGG-19, and Inception-v3 models were applied to identify healthy and six lung diseases (fibrosis, lung opacity, viral pneumonia, bacterial pneumonia, COVID-19, and tuberculosis). Accuracy, precision, recall, f1 score, area under the curve (AUC), and testing time were used to evaluate the performance of these four models. The results demonstrated that the proposed CNN model outperformed all other DL models employed for a seven-class classification with an accuracy of 93.15% and average values for precision, recall, f1-score, and AUC of 0.9343, 0.9443, 0.9386, and 0.9939. The CNN model equally performed well when other multiclass classifications including normal and COVID-19 as the common classes were considered, yielding accuracy values of 98%, 97.49%, 97.81%, 96%, and 96.75% for two, three, four, five, and six classes, respectively. The proposed model can also identify COVID-19 with shorter training and testing times compared to other transfer learning models.<\/jats:p>","DOI":"10.3390\/s23094458","type":"journal-article","created":{"date-parts":[[2023,5,4]],"date-time":"2023-05-04T02:03:18Z","timestamp":1683165798000},"page":"4458","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A Real Time Method for Distinguishing COVID-19 Utilizing 2D-CNN and Transfer Learning"],"prefix":"10.3390","volume":"23","author":[{"given":"Abida","family":"Sultana","sequence":"first","affiliation":[{"name":"Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md.","family":"Nahiduzzaman","sequence":"additional","affiliation":[{"name":"Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh"},{"name":"Department of Electrical Engineering, Qatar University, Doha 2713, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sagor Chandro","family":"Bakchy","sequence":"additional","affiliation":[{"name":"Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3350-848X","authenticated-orcid":false,"given":"Saleh Mohammed","family":"Shahriar","sequence":"additional","affiliation":[{"name":"Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hasibul Islam","family":"Peyal","sequence":"additional","affiliation":[{"name":"Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0744-8206","authenticated-orcid":false,"given":"Muhammad E. H.","family":"Chowdhury","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Qatar University, Doha 2713, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7068-9112","authenticated-orcid":false,"given":"Amith","family":"Khandakar","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Qatar University, Doha 2713, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8663-886X","authenticated-orcid":false,"given":"Mohamed","family":"Arselene Ayari","sequence":"additional","affiliation":[{"name":"Department of Civil and Architectural Engineering, Qatar University, Doha 2713, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7300-506X","authenticated-orcid":false,"given":"Mominul","family":"Ahsan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of York, Deramore Lane, Heslington, York YO10 5GH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7010-8285","authenticated-orcid":false,"given":"Julfikar","family":"Haider","sequence":"additional","affiliation":[{"name":"Department of Engineering, Manchester Metropolitan University, Chester Street, Manchester M1 5GD, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1038\/s41586-020-2008-3","article-title":"A new coronavirus associated with human respiratory disease in china","volume":"579","author":"Wu","year":"2020","journal-title":"Nature"},{"key":"ref_2","unstructured":"WHO (2023, April 18). COVID-19 Situation Reports. Available online: https:\/\/www.who.int\/emergencies\/diseases\/novel-coronavirus-2019\/situation-reports."},{"key":"ref_3","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. Autoimmun."},{"key":"ref_4","first-page":"1843","article-title":"Detection of SARS-CoV-2 in different types of clinical specimens","volume":"323","author":"Wang","year":"2020","journal-title":"JAMA"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1002\/jmv.25674","article-title":"Recent advances in the detection of respiratory virus infection in humans","volume":"92","author":"Zhang","year":"2020","journal-title":"J. Med. Virol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1097\/RTI.0000000000000404","article-title":"Added value of ultra\u2013\u00b4 low-dose computed tomography, dose equivalent to chest X-ray radiography, for diagnosing chest pathology","volume":"34","author":"Kroft","year":"2019","journal-title":"J. Thorac. Imaging"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1016\/S0140-6736(20)30211-7","article-title":"Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in wuhan, china: A descriptive study","volume":"395","author":"Chen","year":"2020","journal-title":"Lancet"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.neunet.2020.01.018","article-title":"Theory of deep convolutional neural networks: Downsampling","volume":"124","author":"Zhou","year":"2020","journal-title":"Neural Netw."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"35501","DOI":"10.1109\/ACCESS.2021.3061621","article-title":"Detecting SARS-CoV-2 from chest X-ray using artificial intelligence","volume":"9","author":"Ahsan","year":"2021","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"62110","DOI":"10.1109\/ACCESS.2022.3182498","article-title":"Pneumonia Detection Proposing a Hybrid Deep Convolutional Neural Network Based on Two Parallel Visual Geometry Group Architectures and Machine Learning Classifiers","volume":"10","author":"Yaseliani","year":"2022","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1109\/TMI.2015.2457891","article-title":"A cross-modality learning approach for vessel segmentation in retinal images","volume":"35","author":"Li","year":"2015","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sultana, A., Khan, T.T., and Hossain, T. (2021, January 17\u201319). Comparison of four transfer learning and hybrid cnn models on three types of lung cancer. Proceedings of the 2021 5th International Conference on Electrical Information and Communication Technology (EICT), IEEE, Khulna, Bangladesh.","DOI":"10.1109\/EICT54103.2021.9733614"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"191586","DOI":"10.1109\/ACCESS.2020.3031384","article-title":"Reliable tuberculosis detection using chest X-ray with deep learning, segmentation and visualization","volume":"8","author":"Rahman","year":"2020","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"109944","DOI":"10.1016\/j.chaos.2020.109944","article-title":"Application of deep learning for fast detection of COVID-19 in X-rays using ncovnet","volume":"138","author":"Panwar","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Khan, E., Rehman, M.Z.U., Ahmed, F., Alfouzan, F.A., Alzahrani, N.M., and Ahmad, J. (2022). Chest X-ray classification for the detection of COVID-19 using deep learning techniques. Sensors, 22.","DOI":"10.3390\/s22031211"},{"key":"ref_17","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_18","unstructured":"Hemdan, E.E.-D., Shouman, M.A., and Karar, M.E. (2020). Covidx-net: A framework of deep learning classifiers to diagnose COVID-19 in X-ray images. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"114054","DOI":"10.1016\/j.eswa.2020.114054","article-title":"Deep learning approaches for COVID-19 detection based on chest X-ray images","volume":"164","author":"Ismael","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1109\/JAS.2020.1003393","article-title":"Automatic detection of COVID-19 infection using chest X-ray images through transfer learning","volume":"8","author":"Ohata","year":"2020","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_21","unstructured":"Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C., and Shpanskaya, K. (2017). Chexnet: Radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"132665","DOI":"10.1109\/ACCESS.2020.3010287","article-title":"Can ai help in screening viral and COVID-19 pneumonia?","volume":"8","author":"Chowdhury","year":"2020","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"119905","DOI":"10.1109\/ACCESS.2022.3221531","article-title":"Deep Learning Algorithms for Automatic COVID-19 Detection on Chest X-Ray Images","volume":"10","author":"Cannata","year":"2022","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"105608","DOI":"10.1016\/j.cmpb.2020.105608","article-title":"Explainable deep learning for pulmonary disease and coronavirus COVID-19 detection from X-rays","volume":"196","author":"Brunese","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"103792","DOI":"10.1016\/j.compbiomed.2020.103792","article-title":"Automated detection of COVID-19 cases using deep neural networks with X-ray images","volume":"121","author":"Ozturk","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"113909","DOI":"10.1016\/j.eswa.2020.113909","article-title":"Coronavirus disease (COVID-19) detection in chest X-ray images using majority voting based classifier ensemble","volume":"165","author":"Chandra","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Vantaggiato, E., Paladini, E., Bougourzi, F., Distante, C., Hadid, A., and Taleb-Ahmed, A. (2021). COVID-19 recognition using ensemble-cnns in two new chest X-ray databases. Sensors, 21.","DOI":"10.3390\/s21051742"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"118576","DOI":"10.1016\/j.eswa.2022.118576","article-title":"Chestx-ray6: Prediction of multiple diseases including COVID-19 from chest X-ray images using convolutional neural network","volume":"211","author":"Nahiduzzaman","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Khan, I.U., Aslam, N., Anwar, T., Alsaif, H.S., Chrouf, S.M.B., Alzahrani, N.A., Alamoudi, F.A., Kamaleldin, M.M.A., and Awary, K.B. (2022). Using a deep learning model to explore the impact of clinical data on COVID-19 diagnosis using chest X-ray. Sensors, 22.","DOI":"10.3390\/s22020669"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Umair, M., Khan, M.S., Ahmed, F., Baothman, F., Alqahtani, F., Alian, M., and Ahmad, J. (2021). Detection of COVID-19 using transfer learning and grad-cam visualization on indigenously collected X-ray dataset. Sensors, 21.","DOI":"10.3390\/s21175813"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Alam, N.A., Ahsan, M., Based, M.A., Haider, J., and Kowalski, M. (2021). COVID-19 Detection from Chest X-ray Images Using Feature Fusion and Deep Learning. Sensors, 21.","DOI":"10.3390\/s21041480"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"118029","DOI":"10.1016\/j.eswa.2022.118029","article-title":"Cov-net: A computer-aided diagnosis method for recognizing COVID-19 from chest X-ray images via machine vision","volume":"207","author":"Li","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"108405","DOI":"10.1016\/j.compeleceng.2022.108405","article-title":"COVID-19 identification in chest X-ray images using intelligent multi-level classification scenario","volume":"104","author":"Babukarthik","year":"2022","journal-title":"Comput. Electr. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-020-00392-9","article-title":"Deep learning applications for COVID-19","volume":"8","author":"Shorten","year":"2021","journal-title":"J. Big Data"},{"key":"ref_35","unstructured":"(2021, February 15). COVID-19. Radiography Database. Available online: https:\/\/www.kaggle.com\/datasets\/tawsifurrahman\/covid19-radiography-database."},{"key":"ref_36","unstructured":"(2021, February 20). Pneumonia Virus vs Pneumonia Bacteria. Available online: https:\/\/www.kaggle.com\/datasets\/muhammadmasdar\/pneumonia-virus-vs-pneumonia-bacteria."},{"key":"ref_37","first-page":"651","article-title":"Labeled optical coherence tomography (oct) and chest X-ray images for classification","volume":"2","author":"Kermany","year":"2018","journal-title":"Mendeley Data"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., and Summers, R. (2017, January 21\u201326). ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE CVPR, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.369"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-019-0192-5","article-title":"Survey on deep learning with class imbalance","volume":"6","author":"Johnson","year":"2019","journal-title":"J. Big Data"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-019-0263-7","article-title":"Enlarging smaller images before inputting into convolutional neural network: Zero-padding vs. interpolation","volume":"6","author":"Hashemi","year":"2019","journal-title":"J. Big Data"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Peyal, H.I., Shahriar, S.M., Sultana, A., Jahan, I., and Mondol, M.H. (2021, January 8\u20139). Detection of tomato leaf diseases using transfer learning architectures: A comparative analysis. Proceedings of the 2021 International Conference on Automation, Control and Mechatronics for Industry 4.0 (ACMI), IEEE, Rajshahi, Bangladesh.","DOI":"10.1109\/ACMI53878.2021.9528199"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2657","DOI":"10.1007\/s00500-020-05424-3","article-title":"COVID-chexnet: Hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images","volume":"27","author":"Mohammed","year":"2023","journal-title":"Soft Comput."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Al-Shourbaji, I., Kachare, P.H., Abualigah, L., Abdelhag, M.E., Elnaim, B., Anter, A.M., and Gandomi, A.H. (2023). A deep batch normalized convolution approach for improving COVID-19 detection from chest X-ray images. Pathogens, 12.","DOI":"10.3390\/pathogens12010017"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1016\/j.eng.2020.04.010","article-title":"A deep learning system to screen novel coronavirus disease 2019 pneumonia","volume":"6","author":"Xu","year":"2020","journal-title":"Engineering"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"103848","DOI":"10.1016\/j.bspc.2022.103848","article-title":"Covixnet: A novel and efficient deep learning model for detection of COVID-19 using chest X-ray images","volume":"78","author":"Srivastava","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1007\/s13246-020-00865-4","article-title":"COVID-19: Automatic detection from X-ray images utilizing transfer learning with convolutional neural networks","volume":"43","author":"Apostolopoulos","year":"2020","journal-title":"Phys. Eng. Sci. Med."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"427","DOI":"10.3389\/fmed.2020.00427","article-title":"Deep learning-based decision-tree classifier for COVID-19 diagnosis from chest X-ray imaging","volume":"7","author":"Yoo","year":"2020","journal-title":"Front. Med."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"110495","DOI":"10.1016\/j.chaos.2020.110495","article-title":"Corodet: A deep learning based classification for COVID-19 detection using chest X-ray images","volume":"142","author":"Hussain","year":"2021","journal-title":"Chaos Solitons Fractals"},{"key":"ref_50","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_51","unstructured":"Al-Timemy, H., Khushaba, R.N., Mosa, Z.M., and Escudero, J. (2021). Artificial Intelligence for COVID-19, Springer."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4458\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:28:35Z","timestamp":1760124515000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4458"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,3]]},"references-count":51,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["s23094458"],"URL":"https:\/\/doi.org\/10.3390\/s23094458","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,3]]}}}