{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T11:05:42Z","timestamp":1790420742380,"version":"4.1.0"},"reference-count":46,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T00:00:00Z","timestamp":1678320000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>COVID-19 is highly infectious and causes acute respiratory disease. Machine learning (ML) and deep learning (DL) models are vital in detecting disease from computerized chest tomography (CT) scans. The DL models outperformed the ML models. For COVID-19 detection from CT scan images, DL models are used as end-to-end models. Thus, the performance of the model is evaluated for the quality of the extracted feature and classification accuracy. There are four contributions included in this work. First, this research is motivated by studying the quality of the extracted feature from the DL by feeding these extracted to an ML model. In other words, we proposed comparing the end-to-end DL model performance against the approach of using DL for feature extraction and ML for the classification of COVID-19 CT scan images. Second, we proposed studying the effect of fusing extracted features from image descriptors, e.g., Scale-Invariant Feature Transform (SIFT), with extracted features from DL models. Third, we proposed a new Convolutional Neural Network (CNN) to be trained from scratch and then compared to the deep transfer learning on the same classification problem. Finally, we studied the performance gap between classic ML models against ensemble learning models. The proposed framework is evaluated using a CT dataset, where the obtained results are evaluated using five different metrics The obtained results revealed that using the proposed CNN model is better than using the well-known DL model for the purpose of feature extraction. Moreover, using a DL model for feature extraction and an ML model for the classification task achieved better results in comparison to using an end-to-end DL model for detecting COVID-19 CT scan images. Of note, the accuracy rate of the former method improved by using ensemble learning models instead of the classic ML models. The proposed method achieved the best accuracy rate of 99.39%.<\/jats:p>","DOI":"10.1371\/journal.pone.0282608","type":"journal-article","created":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T18:33:38Z","timestamp":1678386818000},"page":"e0282608","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":21,"title":["A hybrid CNN and ensemble model for COVID-19 lung infection detection on chest CT scans"],"prefix":"10.1371","volume":"18","author":[{"given":"Ahmed A.","family":"Akl","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8065-8977","authenticated-orcid":true,"given":"Khalid M.","family":"Hosny","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1790-8640","authenticated-orcid":true,"given":"Mostafa M.","family":"Fouda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmad","family":"Salah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2023,3,9]]},"reference":[{"key":"pone.0282608.ref001","doi-asserted-by":"crossref","first-page":"103795","DOI":"10.1016\/j.compbiomed.2020.103795","article-title":"Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks","author":"AA Ardakani","year":"2020","journal-title":"Computers in Biology and Medicine"},{"key":"pone.0282608.ref002","article-title":"The limited sensitivity of chest computed tomography relative to reverse transcription polymerase chain reaction for Severe Acute Respiratory Syndrome Coronavirus-2 infection: a systematic review on COVID-19 diagnostics","author":"JV Waller","year":"2020","journal-title":"Investigative radiology"},{"key":"pone.0282608.ref003","first-page":"1","article-title":"Can Chest CT Features Distinguish Patients With Negative From Those With Positive Initial RT-PCR Results for Coronavirus Disease (COVID-19)?","author":"D Chen","year":"2020","journal-title":"American Journal of Roentgenology"},{"key":"pone.0282608.ref004","unstructured":"Long M, Zhu H, Wang J, Jordan MI. 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