{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T22:59:40Z","timestamp":1773874780437,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,10,2]],"date-time":"2022-10-02T00:00:00Z","timestamp":1664668800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Academics and the health community are paying much attention to developing smart remote patient monitoring, sensors, and healthcare technology. For the analysis of medical scans, various studies integrate sophisticated deep learning strategies. A smart monitoring system is needed as a proactive diagnostic solution that may be employed in an epidemiological scenario such as COVID-19. Consequently, this work offers an intelligent medicare system that is an IoT-empowered, deep learning-based decision support system (DSS) for the automated detection and categorization of infectious diseases (COVID-19 and pneumothorax). The proposed DSS system was evaluated using three independent standard-based chest X-ray scans. The suggested DSS predictor has been used to identify and classify areas on whole X-ray scans with abnormalities thought to be attributable to COVID-19, reaching an identification and classification accuracy rate of 89.58% for normal images and 89.13% for COVID-19 and pneumothorax. With the suggested DSS system, a judgment depending on individual chest X-ray scans may be made in approximately 0.01 s. As a result, the DSS system described in this study can forecast at a pace of 95 frames per second (FPS) for both models, which is near to real-time.<\/jats:p>","DOI":"10.3390\/s22197474","type":"journal-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T03:07:28Z","timestamp":1665371248000},"page":"7474","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["An Intelligent Sensor Based Decision Support System for Diagnosing Pulmonary Ailment through Standardized Chest X-ray Scans"],"prefix":"10.3390","volume":"22","author":[{"given":"Shivani","family":"Batra","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, KIET Group of Institutions, Delhi-NCR, Ghaziabad 201206, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2288-0240","authenticated-orcid":false,"given":"Harsh","family":"Sharma","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, KIET Group of Institutions, Delhi-NCR, Ghaziabad 201206, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2133-0757","authenticated-orcid":false,"given":"Wadii","family":"Boulila","sequence":"additional","affiliation":[{"name":"Robotics and Internet-of-Things Laboratory, Prince Sultan University, Riyadh 12435, Saudi Arabia"},{"name":"RIADI Laboratory, National School of Computer Sciences, University of Manouba, Manouba 2010, Tunisia"}]},{"given":"Vaishali","family":"Arya","sequence":"additional","affiliation":[{"name":"School of Engineering, GD Goenka University, Gurugram 122103, India"}]},{"given":"Prakash","family":"Srivastava","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Graphic Era (Deemed to Be University), Dehradun 248002, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2409-7172","authenticated-orcid":false,"given":"Mohammad","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information, Taibah University, Medina 42353, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8873-9755","authenticated-orcid":false,"given":"Moez","family":"Krichen","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science & IT, Al Baha University, Al Baha 65779, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1152\/physiolgenomics.00029.2020","article-title":"Artificial intelligence and machine learning to fight COVID-19","volume":"52","author":"Alimadadi","year":"2020","journal-title":"Physiol. Genom."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1126\/science.aba9757","article-title":"The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak","volume":"368","author":"Chinazzi","year":"2020","journal-title":"Science"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"103670","DOI":"10.1016\/j.compbiomed.2020.103670","article-title":"Computers and viral diseases. Preliminary bioinformatics studies on the design of a synthetic vaccine and a preventative peptidomimetic antagonist against the SARS-CoV-2 (2019-nCoV, COVID-19) coronavirus","volume":"119","author":"Robson","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.ijid.2020.01.050","article-title":"Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak","volume":"92","author":"Zhao","year":"2020","journal-title":"Int. J. Infect. Dis."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Batra, S., Khurana, R., Khan, M.Z., Boulila, W., Koubaa, A., and Srivastava, P. (2022). A Pragmatic Ensemble Strategy for Missing Values Imputation in Health Records. Entropy, 24.","DOI":"10.3390\/e24040533"},{"key":"ref_7","unstructured":"(2021, June 17). Coronavirus Disease (COVID-19) World Health Organization. Available online: https:\/\/www.who.int\/emergencies\/diseases\/novel-coronavirus-2019."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Pathak, A., Batra, S., and Sharma, V. (2021, January 10\u201311). An Assessment of the Missing Data Imputation Techniques for COVID-19 Data. Proceedings of the 3rd International Conference on Machine Learning, Advances in Computing, Renewable Energy and Communication, Krishna Engineering College, Ghaziabad, India.","DOI":"10.1007\/978-981-19-2828-4_62"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"105532","DOI":"10.1016\/j.cmpb.2020.105532","article-title":"COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios","volume":"194","author":"Pereira","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.diii.2020.11.008","article-title":"Radiology indispensable for tracking COVID-19","volume":"102","author":"Li","year":"2021","journal-title":"Diagn. Interv. Imaging"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"E98","DOI":"10.1148\/radiol.2020203465","article-title":"Chest CT in the Emergency Department for Diagnosis of COVID-19 Pneumonia: Dutch Experience","volume":"298","author":"Schalekamp","year":"2021","journal-title":"Radiology"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1016\/j.diii.2021.05.006","article-title":"Imaging of COVID-19: An update of current evidences","volume":"102","author":"Kato","year":"2021","journal-title":"Diagn. Interv. Imaging"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1007\/s10044-021-00958-0","article-title":"Coronavirus disease 2019 (COVID-19): Survival analysis using deep learning and Cox regression model","volume":"24","author":"Atlam","year":"2021","journal-title":"Pattern Anal. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.cmpb.2018.01.017","article-title":"Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system","volume":"157","author":"Park","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.ijmedinf.2018.06.003","article-title":"A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification","volume":"117","author":"Choi","year":"2018","journal-title":"Int. J. Med. Informat."},{"key":"ref_17","first-page":"6657533","article-title":"Pneumothorax in mechanically ventilated patients with COVID-19 infection","volume":"2021","author":"Akdogan","year":"2021","journal-title":"Case Rep. Crit Care"},{"key":"ref_18","first-page":"101265","article-title":"Pneumothorax in COVID-19 pneumonia: A case series","volume":"31","author":"Hameed","year":"2020","journal-title":"Respir. Med. Case Rep."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2345","DOI":"10.1378\/chest.125.6.2345","article-title":"Severe acute respiratory syndrome complicated by spontaneous pneumothorax","volume":"125","author":"Sihoe","year":"2004","journal-title":"Chest"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1007\/s10140-020-01806-0","article-title":"Pneumomediastinum and spontaneous pneumothorax as an extrapulmonary complication of COVID-19 disease","volume":"27","year":"2020","journal-title":"Emerg. Radiol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"E18","DOI":"10.1148\/radiol.2020202439","article-title":"Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence","volume":"298","author":"Lessmann","year":"2021","journal-title":"Radiology"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Karpiel, I., Starcevic, A., and Urzeniczok, M. (2022). Database and AI Diagnostic Tools Improve Understanding of Lung Damage, Correlation of Pulmonary Disease and Brain Damage in COVID-19. Sensors, 22.","DOI":"10.3390\/s22166312"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"104791","DOI":"10.1016\/j.ijmedinf.2022.104791","article-title":"The accuracy of machine learning approaches using non-image data for the prediction of COVID-19: A meta-analysis","volume":"164","author":"Kuo","year":"2022","journal-title":"Int. J. Med. Inf."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Fusco, R., Grassi, R., Granata, V., Setola, S.V., Grassi, F., Cozzi, D., Pecori, B., Izzo, F., and Petrillo, A. (2021). Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment. J. Pers. Med., 11.","DOI":"10.3390\/jpm11100993"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2688","DOI":"10.1109\/TMI.2020.2993291","article-title":"Deep learning COVID-19 features on CXR using limited training data sets","volume":"39","author":"Oh","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2626","DOI":"10.1109\/TMI.2020.2996645","article-title":"Inf-net: Automatic covid-19 lung infection segmentation from ct images","volume":"39","author":"Fan","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"19549","DOI":"10.1038\/s41598-020-76550-z","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_28","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_29","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_30","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1007\/s10489-020-01826-w","article-title":"Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices","volume":"51","author":"Ahuja","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_31","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_32","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_33","doi-asserted-by":"crossref","first-page":"3913","DOI":"10.1007\/s10489-020-01770-9","article-title":"A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19","volume":"50","author":"Mohamadou","year":"2020","journal-title":"Appl. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Al-kahtani, M.S., Khan, F., and Taekeun, W. (2022). Application of Internet of Things and Sensors in Healthcare. Sensors, 22.","DOI":"10.3390\/s22155738"},{"key":"ref_35","unstructured":"Shoeibi, A., Khodatars, M., Alizadehsani, R., Ghassemi, N., Jafari, M., Moridian, P., Khadem, A., Sadeghi, D., Hussain, S., and Zare, A. (2020). Automated detection and forecasting of covid-19 using deep learning techniques: A review. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"105584","DOI":"10.1016\/j.cmpb.2020.105584","article-title":"Evaluation of deep learning detection and classification towards computer-aided diagnosis of breast lesions in digital X-ray mammograms","volume":"196","author":"Han","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2511","DOI":"10.1002\/jmv.25891","article-title":"Analysis of 92 deceased patients with COVID-19","volume":"92","author":"Yang","year":"2020","journal-title":"J. Med. Virol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"E252","DOI":"10.1148\/radiol.2020202352","article-title":"High incidence of barotrauma in patients with COVID-19 infection on invasive mechanical ventilation","volume":"297","author":"McGuinness","year":"2020","journal-title":"Radiology"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2002697","DOI":"10.1183\/13993003.02697-2020","article-title":"COVID-19 and pneumothorax: A multicentre retrospective case series","volume":"56","author":"Martinelli","year":"2020","journal-title":"Eur. Respir. J."},{"key":"ref_40","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 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"16942","DOI":"10.1038\/s41598-020-74164-z","article-title":"A comprehensive study on classification of COVID-19 on computed tomography with pretrained convolutional neural networks","volume":"10","author":"Pham","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., and Summers, R.M. (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, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.369"},{"key":"ref_43","unstructured":"(2021, June 17). SIIM-ACR Pneumothorax Segmentation. Available online: https:\/\/www.kaggle.com\/c\/siim-acr-pneumothorax-segmentation."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"102216","DOI":"10.1016\/j.media.2021.102216","article-title":"AIforCOVID: Predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study","volume":"74","author":"Soda","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","article-title":"Identifying medical diagnoses and treatable diseases by image-based deep learning","volume":"172","author":"Kermany","year":"2018","journal-title":"Cell"},{"key":"ref_46","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_47","first-page":"222","article-title":"October. Improving spatiotemporal change detection: A high level fusion approach for discovering uncertain knowledge from satellite image databases","volume":"9","author":"Boulila","year":"2009","journal-title":"ICDM"},{"key":"ref_48","unstructured":"Boulila, W., Farah, I.R., Ettabaa, K.S., Solaiman, B., and Gh\u00e9zala, H.B. (2010). Spatio-Temporal Modeling for Knowledge Discovery in Satellite Image Databases. CORIA, 35\u201349."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.ecoinf.2016.11.006","article-title":"Propagating aleatory and epistemic uncertainty in land cover change prediction process","volume":"37","author":"Ferchichi","year":"2017","journal-title":"Ecol. Inform."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.jocs.2017.10.006","article-title":"Sensitivity analysis approach to model epistemic and aleatory imperfection: Application to Land Cover Change prediction model","volume":"23","author":"Boulila","year":"2017","journal-title":"J. Comput. Sci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/19\/7474\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:45:33Z","timestamp":1760143533000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/19\/7474"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,2]]},"references-count":50,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["s22197474"],"URL":"https:\/\/doi.org\/10.3390\/s22197474","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,2]]}}}