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Moreover, the majority of these methods are evaluated by using CXR data from a single source, which makes them prohibitively expensive. On a variety of datasets, current methods for COVID-19 detection may not perform as well. Moreover, most current approaches focus on COVID-19 detection. This study introduces a rapid and lightweight MobileNetV2-based model for accurate recognition of COVID-19 based on CXR images; this is done by using machine vision algorithms that focused largely on robust and potent feature-learning capabilities. The proposed model is assessed by using a dataset obtained from various sources. In addition to COVID-19, the dataset includes bacterial and viral pneumonia. This model is capable of identifying COVID-19, as well as other lung disorders, including bacterial and viral pneumonia, among others. Experiments with each model were thoroughly analyzed. According to the findings of this investigation, MobileNetv2, with its 92% and 93% training validity and 88% precision, was the most applicable and reliable model for this diagnosis. As a result, one may infer that this study has practical value in terms of giving a reliable reference to the radiologist and theoretical significance in terms of establishing strategies for developing robust features with great presentation ability.&lt;\/p&gt;\n\t\t&lt;\/abstract&gt;<\/jats:p>","DOI":"10.3934\/mbe.2023368","type":"journal-article","created":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T10:41:30Z","timestamp":1678099290000},"page":"8400-8427","source":"Crossref","is-referenced-by-count":6,"title":["An efficient, lightweight MobileNetV2-based fine-tuned model for COVID-19 detection using chest X-ray images"],"prefix":"10.3934","volume":"20","author":[{"given":"Shubashini","family":"Velu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2321","reference":[{"key":"key-10.3934\/mbe.2023368-1","doi-asserted-by":"publisher","unstructured":"A. A. Abdelhamid, E. Abdelhalim, M. A. Mohamed, F. 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