{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T17:02:51Z","timestamp":1781370171945,"version":"3.54.1"},"reference-count":36,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,5,17]],"date-time":"2021-05-17T00:00:00Z","timestamp":1621209600000},"content-version":"vor","delay-in-days":136,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002383","name":"King Saud University","doi-asserted-by":"publisher","award":["RG-1441-455"],"award-info":[{"award-number":["RG-1441-455"]}],"id":[{"id":"10.13039\/501100002383","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Artificial intelligence (AI) techniques in general and convolutional neural networks (CNNs) in particular have attained successful results in medical image analysis and classification. A deep CNN architecture has been proposed in this paper for the diagnosis of COVID\u201019 based on the chest X\u2010ray image classification. Due to the nonavailability of sufficient\u2010size and good\u2010quality chest X\u2010ray image dataset, an effective and accurate CNN classification was a challenge. To deal with these complexities such as the availability of a very\u2010small\u2010sized and imbalanced dataset with image\u2010quality issues, the dataset has been preprocessed in different phases using different techniques to achieve an effective training dataset for the proposed CNN model to attain its best performance. The preprocessing stages of the datasets performed in this study include dataset balancing, medical experts\u2019 image analysis, and data augmentation. The experimental results have shown the overall accuracy as high as 99.5% which demonstrates the good capability of the proposed CNN model in the current application domain. The CNN model has been tested in two scenarios. In the first scenario, the model has been tested using the 100 X\u2010ray images of the original processed dataset which achieved an accuracy of 100%. In the second scenario, the model has been tested using an independent dataset of COVID\u201019 X\u2010ray images. The performance in this test scenario was as high as 99.5%. To further prove that the proposed model outperforms other models, a comparative analysis has been done with some of the machine learning algorithms. The proposed model has outperformed all the models generally and specifically when the model testing was done using an independent testing set.<\/jats:p>","DOI":"10.1155\/2021\/6621607","type":"journal-article","created":{"date-parts":[[2021,5,18]],"date-time":"2021-05-18T00:50:06Z","timestamp":1621299006000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":137,"title":["An Efficient CNN Model for COVID\u201019 Disease Detection Based on X\u2010Ray Image Classification"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0795-2835","authenticated-orcid":false,"given":"Aijaz Ahmad","family":"Reshi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8403-1047","authenticated-orcid":false,"given":"Furqan","family":"Rustam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5822-4005","authenticated-orcid":false,"given":"Arif","family":"Mehmood","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdulaziz","family":"Alhossan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyad","family":"Alrabiah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6295-8685","authenticated-orcid":false,"given":"Ajaz","family":"Ahmad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hessa","family":"Alsuwailem","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0854-768X","authenticated-orcid":false,"given":"Gyu Sang","family":"Choi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,5,17]]},"reference":[{"key":"e_1_2_11_1_2","first-page":"157","article-title":"WHO declares COVID-19 a pandemic","volume":"91","author":"Cucinotta D.","year":"2020","journal-title":"Acta Biomedica: Atenei Parmensis"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2997311"},{"key":"e_1_2_11_3_2","unstructured":"CennimoD. 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