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DC Field | Value | Language |
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dc.contributor.author | Micallef, Neil | - |
dc.contributor.author | Debono, Carl James | - |
dc.contributor.author | Seychell, Dylan | - |
dc.contributor.author | Attard, Conrad | - |
dc.date.accessioned | 2022-10-25T05:09:14Z | - |
dc.date.available | 2022-10-25T05:09:14Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | Micallef, N., Debono, C. J., Seychell, D., & Attard, C. (2022). Automatic Detection of COVID-19 Pneumonia in Chest Computed Tomography Scans Using Convolutional Neural Networks. 2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON), Palermo. 1118-1123. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/102998 | - |
dc.description.abstract | The Coronavirus outbreak caused by the SARSCoV-2 virus has been the focal point of global attention over the past two years, owing to the pandemic’s infection rate and the huge burden on the world’s healthcare systems and economy. Diagnosis of infection by the virus may be carried out through a number of tests, with the current mainly used technique being reverse transcription polymerase chain reaction tests. An alternative approach for diagnosis is through the use of medical imagery such as chest X-rays or chest Computed Tomography images. In this work, we propose a machine learning driven approach which automatically detects pulmonary pathological features caused by the Coronavirus infection in chest Computed Tomography images. The model was trained and evaluated on the COVIDx CT-2A dataset, achieving an accuracy of 96.31% on the testing segment of the dataset. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | COVID-19 (Disease) | en_GB |
dc.subject | Coronavirus infections | en_GB |
dc.subject | Tomography | en_GB |
dc.subject | Deep learning (Machine learning) | en_GB |
dc.title | Automatic detection of covid-19 pneumonia in chest computed tomography scans using convolutional neural networks | en_GB |
dc.type | conferenceObject | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder | en_GB |
dc.bibliographicCitation.conferencename | 2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON) | en_GB |
dc.bibliographicCitation.conferenceplace | Palermo, Italy, 14-16/06/2022. | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
dc.identifier.doi | 10.1109/MELECON53508.2022.9843100 | - |
Appears in Collections: | Scholarly Works - FacICTAI |
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Automatic_Detection_of_COVID-19_Pneumonia_in_chest_computed_tomography_scans_using_convolutional_neural_networks.pdf Restricted Access | 1.68 MB | Adobe PDF | View/Open Request a copy |
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