A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain

dc.authoridHeidari, Arash/0000-0003-4279-8551
dc.authoridJafari Navimipour, Nima/0000-0002-5514-5536
dc.authoridToumaj, Shiva/0000-0002-4828-9427
dc.authorwosidHeidari, Arash/AAK-9761-2021
dc.authorwosidJafari Navimipour, Nima/AAF-5662-2021
dc.contributor.authorHeidari, Arash
dc.contributor.authorToumaj, Shiva
dc.contributor.authorNavimipour, Nima Jafari
dc.contributor.authorUnal, Mehmet
dc.date.accessioned2023-10-19T15:12:13Z
dc.date.available2023-10-19T15:12:13Z
dc.date.issued2022
dc.department-temp[Heidari, Arash] Islamic Azad Univ, Dept Comp Engn, Tabriz Branch, Tabriz, Iran; [Heidari, Arash] Islamic Azad Univ, Dept Comp Engn, Shabestar Branch, Shabestar, Iran; [Navimipour, Nima Jafari] Kadir Has Univ, Dept Comp Engn, Istanbul, Turkey; [Toumaj, Shiva] Urmia Univ Med Sci, Orumiyeh, Iran; [Unal, Mehmet] Nisantasi Univ, Dept Comp Engn, Istanbul, Turkeyen_US
dc.description.abstractWith the global spread of the COVID-19 epidemic, a reliable method is required for identifying COVID-19 victims. The biggest issue in detecting the virus is a lack of testing kits that are both reliable and affordable. Due to the virus's rapid dissemination, medical professionals have trouble finding positive patients. However, the next real-life issue is sharing data with hospitals around the world while considering the organizations' privacy concerns. The primary worries for training a global Deep Learning (DL) model are creating a collaborative platform and personal confidentiality. Another challenge is exchanging data with health care institutions while protecting the organizations' confidentiality. The primary concerns for training a universal DL model are creating a collaborative platform and preserving privacy. This paper provides a model that receives a small quantity of data from various sources, like organizations or sections of hospitals, and trains a global DL model utilizing blockchain-based Convolutional Neural Networks (CNNs). In addition, we use the Transfer Learning (TL) technique to initialize layers rather than initialize randomly and discover which layers should be removed before selection. Besides, the blockchain system verifies the data, and the DL method trains the model globally while keeping the institution's confidentiality. Furthermore, we gather the actual and novel COVID-19 patients. Finally, we run extensive experiments utilizing Python and its libraries, such as Scikit-Learn and TensorFlow, to assess the proposed method. We evaluated works using five different datasets, including Boukan Dr. Shahid Gholipour hospital, Tabriz Emam Reza hospital, Mahabad Emam Khomeini hospital, Maragheh Dr.Beheshti hospital, and Miandoab Abbasi hospital datasets, and our technique outperform state-of-the-art methods on average in terms of precision (2.7%), recall (3.1%), F1 (2.9%), and accuracy (2.8%).en_US
dc.identifier.citation39
dc.identifier.doi10.1016/j.compbiomed.2022.105461en_US
dc.identifier.issn0010-4825
dc.identifier.issn1879-0534
dc.identifier.pmid35366470en_US
dc.identifier.scopus2-s2.0-85127131173en_US
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.compbiomed.2022.105461
dc.identifier.urihttps://hdl.handle.net/20.500.12469/5378
dc.identifier.volume145en_US
dc.identifier.wosWOS:000819697000005en_US
dc.identifier.wosqualityQ1
dc.khas20231019-WoSen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofComputers in Biology and Medicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBlockchainen_US
dc.subjectDeep learningen_US
dc.subjectChest CTen_US
dc.subjectNetEn_Us
dc.subjectCNNen_US
dc.subjectCOVID-19en_US
dc.subjectNet
dc.subjectTransfer learningen_US
dc.titleA privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchainen_US
dc.typeArticleen_US
dspace.entity.typePublication

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