{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T20:51:30Z","timestamp":1777495890860,"version":"3.51.4"},"reference-count":71,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,20]],"date-time":"2021-02-20T00:00:00Z","timestamp":1613779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Dr. Marcin Kowalski","award":["NA"],"award-info":[{"award-number":["NA"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Currently, COVID-19 is considered to be the most dangerous and deadly disease for the human body caused by the novel coronavirus. In December 2019, the coronavirus spread rapidly around the world, thought to be originated from Wuhan in China and is responsible for a large number of deaths. Earlier detection of the COVID-19 through accurate diagnosis, particularly for the cases with no obvious symptoms, may decrease the patient\u2019s death rate. Chest X-ray images are primarily used for the diagnosis of this disease. This research has proposed a machine vision approach to detect COVID-19 from the chest X-ray images. The features extracted by the histogram-oriented gradient (HOG) and convolutional neural network (CNN) from X-ray images were fused to develop the classification model through training by CNN (VGGNet). Modified anisotropic diffusion filtering (MADF) technique was employed for better edge preservation and reduced noise from the images. A watershed segmentation algorithm was used in order to mark the significant fracture region in the input X-ray images. The testing stage considered generalized data for performance evaluation of the model. Cross-validation analysis revealed that a 5-fold strategy could successfully impair the overfitting problem. This proposed feature fusion using the deep learning technique assured a satisfactory performance in terms of identifying COVID-19 compared to the immediate, relevant works with a testing accuracy of 99.49%, specificity of 95.7% and sensitivity of 93.65%. When compared to other classification techniques, such as ANN, KNN, and SVM, the CNN technique used in this study showed better classification performance. K-fold cross-validation demonstrated that the proposed feature fusion technique (98.36%) provided higher accuracy than the individual feature extraction methods, such as HOG (87.34%) or CNN (93.64%).<\/jats:p>","DOI":"10.3390\/s21041480","type":"journal-article","created":{"date-parts":[[2021,2,21]],"date-time":"2021-02-21T21:15:01Z","timestamp":1613942101000},"page":"1480","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":145,"title":["COVID-19 Detection from Chest X-ray Images Using Feature Fusion and Deep Learning"],"prefix":"10.3390","volume":"21","author":[{"given":"Nur-A-Alam","family":"Alam","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail 1902, Bangladesh"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7300-506X","authenticated-orcid":false,"given":"Mominul","family":"Ahsan","sequence":"additional","affiliation":[{"name":"Department of Engineering, Manchester Metropolitan University, Chester St, Manchester M1 5GD, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7702-974X","authenticated-orcid":false,"given":"Md. Abdul","family":"Based","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronics and Telecommunication Engineering, Dhaka International University, Dhaka 1205, Bangladesh"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7010-8285","authenticated-orcid":false,"given":"Julfikar","family":"Haider","sequence":"additional","affiliation":[{"name":"Department of Engineering, Manchester Metropolitan University, Chester St, Manchester M1 5GD, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1361-9828","authenticated-orcid":false,"given":"Marcin","family":"Kowalski","sequence":"additional","affiliation":[{"name":"Institute of Optoelectronics, Military University of Technology, Gen. S. Kaliskiego, 00-908 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,20]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"1708","DOI":"10.1056\/NEJMoa2002032","article-title":"Clinical characteristics of coronavirus disease 2019 in China","volume":"382","author":"Guan","year":"2020","journal-title":"N. Engl. J. 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