{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T22:39:44Z","timestamp":1782427184194,"version":"3.54.5"},"reference-count":39,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T00:00:00Z","timestamp":1684454400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002383","name":"King Saud University","doi-asserted-by":"publisher","award":["RSP2023R34"],"award-info":[{"award-number":["RSP2023R34"]}],"id":[{"id":"10.13039\/501100002383","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>COVID-19 is an infectious disease caused by the deadly virus SARS-CoV-2 that affects the lung of the patient. Different symptoms, including fever, muscle pain and respiratory syndrome, can be identified in COVID-19-affected patients. The disease needs to be diagnosed in a timely manner, otherwise the lung infection can turn into a severe form and the patient\u2019s life may be in danger. In this work, an ensemble deep learning-based technique is proposed for COVID-19 detection that can classify the disease with high accuracy, efficiency, and reliability. A weighted average ensemble (WAE) prediction was performed by combining three CNN models, namely Xception, VGG19 and ResNet50V2, where 97.25% and 94.10% accuracy was achieved for binary and multiclass classification, respectively. To accurately detect the disease, different test methods have been proposed and developed, some of which are even being used in real-time situations. RT-PCR is one of the most successful COVID-19 detection methods, and is being used worldwide with high accuracy and sensitivity. However, complexity and time-consuming manual processes are limitations of this method. To make the detection process automated, researchers across the world have started to use deep learning to detect COVID-19 applied on medical imaging. Although most of the existing systems offer high accuracy, different limitations, including high variance, overfitting and generalization errors, can be found that can degrade the system performance. Some of the reasons behind those limitations are a lack of reliable data resources, missing preprocessing techniques, a lack of proper model selection, etc., which eventually create reliability issues. Reliability is an important factor for any healthcare system. Here, transfer learning with better preprocessing techniques applied on two benchmark datasets makes the work more reliable. The weighted average ensemble technique with hyperparameter tuning ensures better accuracy than using a randomly selected single CNN model.<\/jats:p>","DOI":"10.3390\/diagnostics13101806","type":"journal-article","created":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T10:35:11Z","timestamp":1684492511000},"page":"1806","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A Novel Deep Learning-Based Classification Framework for COVID-19 Assisted with Weighted Average Ensemble Modeling"],"prefix":"10.3390","volume":"13","author":[{"given":"Gouri Shankar","family":"Chakraborty","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, Punjab, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Salil","family":"Batra","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, Punjab, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6571-327X","authenticated-orcid":false,"given":"Aman","family":"Singh","sequence":"additional","affiliation":[{"name":"Higher Polytechnic School, Universidad Europea del Atl\u00e1ntico, C\/Isabel Torres 21, 39011 Santander, Spain"},{"name":"Department of Engineering, Universidad Internacional Iberoamericana, Arecibo, PR 00613, USA"},{"name":"Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, Uttarakhand, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9781-3969","authenticated-orcid":false,"given":"Ghulam","family":"Muhammad","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4412-2491","authenticated-orcid":false,"given":"Vanessa Yelamos","family":"Torres","sequence":"additional","affiliation":[{"name":"Department of Engineering, Universidad Internacional Iberoamericana, Arecibo, PR 00613, USA"},{"name":"Engineering Research & Innovation Group, Universidad Europea del Atl\u00e1ntico, C\/Isabel Torres 21, 39011 Santander, Spain"},{"name":"Department of Project Management, Universidad Internacional Iberoamericana, Campeche C.P. 24560, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Makul","family":"Mahajan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, Punjab, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,19]]},"reference":[{"key":"ref_1","unstructured":"Areej, A., Wahab, A.M., and Ashraf, Y.M. 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