dc.contributor.author | Muhammad, Khan | |
dc.contributor.author | Ullah, Hayat | |
dc.contributor.author | Khan, Zulfiqar Ahmad | |
dc.contributor.author | Saudagar, Abdul Khader Jilani | |
dc.contributor.author | AlTameem, Abdullah | |
dc.contributor.author | AlKhathami, Mohammed | |
dc.contributor.author | Khan, Muhammad Badruddin | |
dc.contributor.author | Abul Hasanat, Mozaherul Hoque | |
dc.contributor.author | Mahmood Malik, Khalid | |
dc.contributor.author | Hijji, Mohammad | |
dc.contributor.author | Sajjad, Muhammad | |
dc.date.accessioned | 2023-01-18T12:53:56Z | |
dc.date.available | 2023-01-18T12:53:56Z | |
dc.date.created | 2022-05-02T09:00:07Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Frontiers in Oncology. 2022, 11 . | en_US |
dc.identifier.issn | 2234-943X | |
dc.identifier.uri | https://hdl.handle.net/11250/3044321 | |
dc.description.abstract | The coronavirus disease 2019 (COVID-19) pandemic has caused a major outbreak around the world with severe impact on health, human lives, and economy globally. One of the crucial steps in fighting COVID-19 is the ability to detect infected patients at early stages and put them under special care. Detecting COVID-19 from radiography images using computational medical imaging method is one of the fastest ways to diagnose the patients. However, early detection with significant results is a major challenge, given the limited available medical imaging data and conflicting performance metrics. Therefore, this work aims to develop a novel deep learning-based computationally efficient medical imaging framework for effective modeling and early diagnosis of COVID-19 from chest x-ray and computed tomography images. The proposed work presents “WEENet” by exploiting efficient convolutional neural network to extract high-level features, followed by classification mechanisms for COVID-19 diagnosis in medical image data. The performance of our method is evaluated on three benchmark medical chest x-ray and computed tomography image datasets using eight evaluation metrics including a novel strategy of cross-corpse evaluation as well as robustness evaluation, and the results are surpassing state-of-the-art methods. The outcome of this work can assist the epidemiologists and healthcare authorities in analyzing the infected medical chest x-ray and computed tomography images, management of the COVID-19 pandemic, bridging the early diagnosis, and treatment gap for Internet of Medical Things environments. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Frontiers Media | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | WEENet: An Intelligent System for Diagnosing COVID-19 and Lung Cancer in IoMT Environments | en_US |
dc.title.alternative | WEENet: An Intelligent System for Diagnosing COVID-19 and Lung Cancer in IoMT Environments | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.source.pagenumber | 13 | en_US |
dc.source.volume | 11 | en_US |
dc.source.journal | Frontiers in Oncology | en_US |
dc.identifier.doi | 10.3389/fonc.2021.811355 | |
dc.identifier.cristin | 2020487 | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |