{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T21:45:03Z","timestamp":1778708703587,"version":"3.51.4"},"reference-count":47,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,9]],"date-time":"2021-12-09T00:00:00Z","timestamp":1639008000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University","award":["RG-21-07-08"],"award-info":[{"award-number":["RG-21-07-08"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61370073"],"award-info":[{"award-number":["61370073"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>COVID-19 is a transferable disease that is also a leading cause of death for a large number of people worldwide. This disease, caused by SARS-CoV-2, spreads very rapidly and quickly affects the respiratory system of the human being. Therefore, it is necessary to diagnosis this disease at the early stage for proper treatment, recovery, and controlling the spread. The automatic diagnosis system is significantly necessary for COVID-19 detection. To diagnose COVID-19 from chest X-ray images, employing artificial intelligence techniques based methods are more effective and could correctly diagnosis it. The existing diagnosis methods of COVID-19 have the problem of lack of accuracy to diagnosis. To handle this problem we have proposed an efficient and accurate diagnosis model for COVID-19. In the proposed method, a two-dimensional Convolutional Neural Network (2DCNN) is designed for COVID-19 recognition employing chest X-ray images. Transfer learning (TL) pre-trained ResNet-50 model weight is transferred to the 2DCNN model to enhanced the training process of the 2DCNN model and fine-tuning with chest X-ray images data for final multi-classification to diagnose COVID-19. In addition, the data augmentation technique transformation (rotation) is used to increase the data set size for effective training of the R2DCNNMC model. The experimental results demonstrated that the proposed (R2DCNNMC) model obtained high accuracy and obtained 98.12% classification accuracy on CRD data set, and 99.45% classification accuracy on CXI data set as compared to baseline methods. This approach has a high performance and could be used for COVID-19 diagnosis in E-Healthcare systems.<\/jats:p>","DOI":"10.3390\/s21248219","type":"journal-article","created":{"date-parts":[[2021,12,9]],"date-time":"2021-12-09T21:46:58Z","timestamp":1639086418000},"page":"8219","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["Diagnostic Approach for Accurate Diagnosis of COVID-19 Employing Deep Learning and Transfer Learning Techniques through Chest X-ray Images Clinical Data in E-Healthcare"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7774-5604","authenticated-orcid":false,"given":"Amin Ul","family":"Haq","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2192-1450","authenticated-orcid":false,"given":"Jian Ping","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3198-7974","authenticated-orcid":false,"given":"Sultan","family":"Ahmad","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7925-9191","authenticated-orcid":false,"given":"Shakir","family":"Khan","sequence":"additional","affiliation":[{"name":"College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammed Ali","family":"Alshara","sequence":"additional","affiliation":[{"name":"College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Reemiah Muneer","family":"Alotaibi","sequence":"additional","affiliation":[{"name":"College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2638","DOI":"10.1109\/TMI.2020.3001810","article-title":"A rapid, accurate and machine-agnostic segmentation and quantification method for CT-based COVID-19 diagnosis","volume":"39","author":"Zhou","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1016\/j.acra.2020.03.003","article-title":"Coronavirus disease (COVID-19): Spectrum of CT findings and temporal progression of the disease","volume":"27","author":"Li","year":"2020","journal-title":"Acad. Radiol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1183\/09031936.01.00213501","article-title":"Imaging of pneumonia: Trends and algorithms","volume":"18","author":"Franquet","year":"2001","journal-title":"Eur. Respir. J."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yasaka, K., and Abe, O. (2018). Deep learning and artificial intelligence in radiology: Current applications and future directions. PLoS Med., 15.","DOI":"10.1371\/journal.pmed.1002707"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Haq, A.U., Li, J.P., Saboor, A., Khan, J., Zhou, W., Jiang, T., Raji, M.F., and Wali, S. (2020, January 18\u201320). 3DCNN: Three-Layers Deep Convolutional Neural Network Architecture for Breast Cancer Detection using Clinical Image Data. Proceedings of the 2020 17th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), Chengdu, China.","DOI":"10.1109\/ICCWAMTIP51612.2020.9317312"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2383","DOI":"10.3233\/JIFS-191461","article-title":"A novel integrated diagnosis method for breast cancer detection","volume":"38","author":"Haq","year":"2020","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Haq, A.U., Li, J.P., Khan, J., Memon, M.H., Nazir, S., Ahmad, S., Khan, G.A., and Ali, A. (2020). Intelligent Machine Learning Approach for Effective Recognition of Diabetes in E-Healthcare Using Clinical Data. Sensors, 20.","DOI":"10.20944\/preprints202002.0462.v1"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"22090","DOI":"10.1109\/ACCESS.2021.3055806","article-title":"Detection of Breast Cancer Through Clinical Data Using Supervised and Unsupervised Feature Selection Techniques","volume":"9","author":"Haq","year":"2021","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Chhikara, P., Singh, P., Gupta, P., and Bhatia, T. (2020). Deep convolutional neural network with transfer learning for detecting pneumonia on chest X-rays. Advances in Bioinformatics, Multimedia, and Electronics Circuits and Signals, Springer.","DOI":"10.1007\/978-981-15-0339-9_13"},{"key":"ref_10","unstructured":"Kermany, D., Zhang, K., and Goldbaum, M. (2018). Large dataset of labeled optical coherence tomography (oct) and chest X-ray images. Mendeley Data, 3."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Saraiva, A.A., Ferreira, N.M.F., de Sousa, L.L., Costa, N.J.C., Sousa, J.V.M., Santos, D., Valente, A., and Soares, S. (2019). Classification of Images of Childhood Pneumonia using Convolutional Neural Networks. Bioimaging, SCITEPRESS\u2014Science and Technology Publications, Lda.","DOI":"10.5220\/0007404301120119"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1016\/j.diii.2012.04.001","article-title":"Benefit of CT scanning for assessing pulmonary disease in the immunodepressed patient","volume":"93","author":"Godet","year":"2012","journal-title":"Diagn. Interv. Imaging"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1097\/MCP.0000000000000567","article-title":"Computed tomography scan contribution to the diagnosis of community-acquired pneumonia","volume":"25","author":"Garin","year":"2019","journal-title":"Curr. Opin. Pulm. Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1016\/S2213-2600(18)30286-8","article-title":"Deep learning for classifying fibrotic lung disease on high-resolution computed tomography: A case-cohort study","volume":"6","author":"Walsh","year":"2018","journal-title":"Lancet Respir. Med."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Garin, N., Marti, C., Carballo, S., Darbellay Farhoumand, P., Montet, X., Roux, X., Scheffler, M., Serratrice, C., Serratrice, J., and Claessens, Y.E. (2019). Rational use of CT-scan for the diagnosis of pneumonia: Comparative accuracy of different strategies. J. Clin. Med., 8.","DOI":"10.3390\/jcm8040514"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.patrec.2019.11.013","article-title":"Deep-learning framework to detect lung abnormality\u2013A study with chest X-ray and lung CT scan images","volume":"129","author":"Bhandary","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"69215","DOI":"10.1109\/ACCESS.2019.2919122","article-title":"Multi-classification of brain tumor images using deep neural network","volume":"7","author":"Sultan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Pereira, S., Meier, R., Alves, V., Reyes, M., and Silva, C.A. (2018). Automatic brain tumor grading from MRI data using convolutional neural networks and quality assessment. Understanding and Interpreting Machine Learning in Medical Image Computing Applications, Springer.","DOI":"10.1007\/978-3-030-02628-8_12"},{"key":"ref_19","unstructured":"Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y. (2016). Deep Learning, MIT Press."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Schwarz, M., Schulz, H., and Behnke, S. (2015, January 26\u201330). RGB-D object recognition and pose estimation based on pre-trained convolutional neural network features. Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA.","DOI":"10.1109\/ICRA.2015.7139363"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"E46","DOI":"10.1148\/radiol.2020200823","article-title":"Performance of radiologists in differentiating COVID-19 from non-COVID-19 viral pneumonia at chest CT","volume":"296","author":"Bai","year":"2020","journal-title":"Radiology"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1007\/s10044-021-00984-y","article-title":"Automatic detection of coronavirus disease (covid-19) using X-ray images and deep convolutional neural networks","volume":"24","author":"Narin","year":"2021","journal-title":"Pattern Anal. Appl."},{"key":"ref_23","first-page":"v5","article-title":"A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)","volume":"14","author":"Wang","year":"2020","journal-title":"medRxiv"},{"key":"ref_24","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_25","unstructured":"Shan, F., Gao, Y., Wang, J., Shi, W., Shi, N., Han, M., Xue, Z., Shen, D., and Shi, Y. (2020). Lung infection quantification of COVID-19 in CT images with deep learning. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2775","DOI":"10.1109\/TCBB.2021.3065361","article-title":"Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images","volume":"18","author":"Song","year":"2021","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"104319","DOI":"10.1016\/j.compbiomed.2021.104319","article-title":"Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images","volume":"132","author":"Tawsifur","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Loddo, A., Pili, F., and Di Ruberto, C. (2021). Deep Learning for COVID-19 Diagnosis from CT Images. Appl. Sci., 11.","DOI":"10.3390\/app11178227"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Gunraj, H., Sabri, A., Koff, D., and Wong, A. (2021). COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse Learning. arXiv.","DOI":"10.3389\/fmed.2021.729287"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"118869","DOI":"10.1109\/ACCESS.2020.3005510","article-title":"Weakly supervised deep learning for covid-19 infection detection and classification from ct images","volume":"8","author":"Hu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_32","unstructured":"Khalifa, N.E.M., Taha, M.H.N., Hassanien, A.E., and Elghamrawy, S. (2020). Detection of coronavirus (COVID-19) associated pneumonia based on generative adversarial networks and a fine-tuned deep transfer learning model using chest X-ray dataset. arXiv."},{"key":"ref_33","first-page":"1","article-title":"Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest X-ray images","volume":"10","author":"Wang","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_34","unstructured":"Cai, J., Lu, L., Xie, Y., Xing, F., and Yang, L. (2017). Improving deep pancreas segmentation in CT and MRI images via recurrent neural contextual learning and direct loss function. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"104348","DOI":"10.1016\/j.compbiomed.2021.104348","article-title":"Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases","volume":"132","author":"Ibrahim","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_36","unstructured":"Bickel, S. (2021, November 24). ECML-PKDD Discovery Challenge 2006 Overview. Available online: https:\/\/www.cs.waikato.ac.nz\/ml\/publications\/2006\/discovery_challenge_proceedings2006.pdf#page=5."},{"key":"ref_37","unstructured":"Ray, S. (2018). Disease classification within dermascopic images using features extracted by resnet50 and classification through deep forest. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"110170","DOI":"10.1016\/j.chaos.2020.110170","article-title":"Diagnosis and detection of infected tissue of COVID-19 patients based on lung X-ray image using convolutional neural network approaches","volume":"140","author":"Hassantabar","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhang, J., Xie, Y., Pang, G., Liao, Z., Verjans, J., Li, W., Sun, Z., He, J., Li, Y., and Shen, C. (2020). Viral pneumonia screening on chest X-ray images using confidence-aware anomaly detection. arXiv.","DOI":"10.1109\/TMI.2020.3040950"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Hall, L.O., Paul, R., Goldgof, D.B., and Goldgof, G.M. (2020). Finding covid-19 from chest X-rays using deep learning on a small dataset. arXiv.","DOI":"10.36227\/techrxiv.12083964"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-021-01745-4","article-title":"Deep learning on chest X-ray images to detect and evaluate pneumonia cases at the era of covid-19","volume":"45","author":"Hammoudi","year":"2021","journal-title":"J. Med. Syst."},{"key":"ref_42","unstructured":"Farooq, M., and Hafeez, A. (2020). Covid-resnet: A deep learning framework for screening of covid19 from radiographs. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1439","DOI":"10.7150\/ijms.46684","article-title":"Efficient GAN-based Chest Radiographs (CXR) augmentation to diagnose coronavirus disease pneumonia","volume":"17","author":"Albahli","year":"2020","journal-title":"Int. J. Med. Sci."},{"key":"ref_44","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_45","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1007\/s10489-020-01831-z","article-title":"Detection of COVID-19 using CXR and CT images using transfer learning and Haralick features","volume":"51","author":"Perumal","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"E65","DOI":"10.1148\/radiol.2020200905","article-title":"Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT","volume":"296","author":"Li","year":"2020","journal-title":"Radiology"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Wong, S., Gatt, A., Stamatescu, V., and McDonnell, M.D. (December, January 30). Understanding Data Augmentation for Classification: When to Warp?. Proceedings of the 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Gold Coast, QLD, Australia.","DOI":"10.1109\/DICTA.2016.7797091"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/24\/8219\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:43:49Z","timestamp":1760168629000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/24\/8219"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,9]]},"references-count":47,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["s21248219"],"URL":"https:\/\/doi.org\/10.3390\/s21248219","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,9]]}}}