A new lung cancer detection method based on the chest CT images using Federated Learning and blockchain systems

dc.authorid Javaheri, Danial/0000-0002-7275-2370
dc.authorid Jafari Navimipour, Nima/0000-0002-5514-5536
dc.authorid Heidari, Arash/0000-0003-4279-8551
dc.authorwosid Javaheri, Danial/AAC-5132-2019
dc.authorwosid Jafari Navimipour, Nima/AAF-5662-2021
dc.authorwosid Heidari, Arash/AAK-9761-2021
dc.contributor.author Jafari Navimipour, Nima
dc.contributor.author Javaheri, Danial
dc.contributor.author Toumaj, Shiva
dc.contributor.author Navimipour, Nima Jafari
dc.contributor.author Rezaei, Mahsa
dc.contributor.author Unal, Mehmet
dc.contributor.other Computer Engineering
dc.date.accessioned 2023-10-19T15:11:36Z
dc.date.available 2023-10-19T15:11:36Z
dc.date.issued 2023
dc.department-temp [Heidari, Arash] Islamic Azad Univ, Dept Comp Engn, Tabriz Branch, Tabriz, Iran; [Javaheri, Danial] Chosun Univ, Dept Comp Engn, Gwangju 61452, South Korea; [Toumaj, Shiva] Urmia Univ Med Sci, Orumiyeh, Iran; [Navimipour, Nima Jafari] Kadir Has Univ, Dept Comp Engn, Istanbul, Turkiye; [Navimipour, Nima Jafari] Natl Yunlin Univ Sci & Technol, Future Technol Res Ctr, Touliu 64002, Yunlin, Taiwan; [Rezaei, Mahsa] Tabriz Univ Med Sci, Fac Surg, Tabriz, Iran; [Unal, Mehmet] Nisantasi Univ, Dept Comp Engn, Istanbul, Turkiye en_US
dc.description.abstract With an estimated five million fatal cases each year, lung cancer is one of the significant causes of death worldwide. Lung diseases can be diagnosed with a Computed Tomography (CT) scan. The scarcity and trustworthiness of human eyes is the fundamental issue in diagnosing lung cancer patients. The main goal of this study is to detect malignant lung nodules in a CT scan of the lungs and categorize lung cancer according to severity. In this work, cutting-edge Deep Learning (DL) algorithms were used to detect the location of cancerous nodules. Also, the real-life issue is sharing data with hospitals around the world while bearing in mind the organizations' privacy issues. Besides, the main problems for training a global DL model are creating a collaborative model and maintaining privacy. This study presented an approach that takes a modest amount of data from multiple hospitals and uses blockchain-based Federated Learning (FL) to train a global DL model. The data were authenticated using blockchain technology, and FL trained the model internationally while maintaining the organization's anonymity. First, we presented a data normalization approach that addresses the variability of data obtained from various institutions using various CT scanners. Furthermore, using a CapsNets method, we classified lung cancer patients in local mode. Finally, we devised a way to train a global model cooperatively utilizing blockchain technology and FL while maintaining anonymity. We also gathered data from real-life lung cancer patients for testing purposes. The suggested method was trained and tested on the Cancer Imaging Archive (CIA) dataset, Kaggle Data Science Bowl (KDSB), LUNA 16, and the local dataset. Finally, we performed extensive experiments with Python and its well-known libraries, such as Scikit-Learn and TensorFlow, to evaluate the suggested method. The findings showed that the method effectively detects lung cancer patients. The technique delivered 99.69 % accuracy with the smallest possible categorization error. en_US
dc.identifier.citationcount 29
dc.identifier.doi 10.1016/j.artmed.2023.102572 en_US
dc.identifier.issn 0933-3657
dc.identifier.issn 1873-2860
dc.identifier.pmid 37295902 en_US
dc.identifier.scopus 2-s2.0-85156244614 en_US
dc.identifier.scopusquality Q1
dc.identifier.uri https://doi.org/10.1016/j.artmed.2023.102572
dc.identifier.uri https://hdl.handle.net/20.500.12469/5124
dc.identifier.volume 141 en_US
dc.identifier.wos WOS:001000573300001 en_US
dc.identifier.wosquality Q1
dc.khas 20231019-WoS en_US
dc.language.iso en en_US
dc.publisher Elsevier en_US
dc.relation.ispartof Artificial Intelligence in Medicine en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 80
dc.subject Blockchain en_US
dc.subject Chest CT en_US
dc.subject CapsNets en_US
dc.subject Profile En_Us
dc.subject Deep Learning en_US
dc.subject Federated Learning en_US
dc.subject Profile
dc.subject Lung cancer en_US
dc.title A new lung cancer detection method based on the chest CT images using Federated Learning and blockchain systems en_US
dc.type Article en_US
dc.wos.citedbyCount 58
dspace.entity.type Publication
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