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

dc.authoridJavaheri, Danial/0000-0002-7275-2370
dc.authoridJafari Navimipour, Nima/0000-0002-5514-5536
dc.authoridHeidari, Arash/0000-0003-4279-8551
dc.authorwosidJavaheri, Danial/AAC-5132-2019
dc.authorwosidJafari Navimipour, Nima/AAF-5662-2021
dc.authorwosidHeidari, Arash/AAK-9761-2021
dc.contributor.authorHeidari, Arash
dc.contributor.authorJavaheri, Danial
dc.contributor.authorToumaj, Shiva
dc.contributor.authorNavimipour, Nima Jafari
dc.contributor.authorRezaei, Mahsa
dc.contributor.authorUnal, Mehmet
dc.date.accessioned2023-10-19T15:11:36Z
dc.date.available2023-10-19T15:11:36Z
dc.date.issued2023
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, Turkiyeen_US
dc.description.abstractWith 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.citation29
dc.identifier.doi10.1016/j.artmed.2023.102572en_US
dc.identifier.issn0933-3657
dc.identifier.issn1873-2860
dc.identifier.pmid37295902en_US
dc.identifier.scopus2-s2.0-85156244614en_US
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.artmed.2023.102572
dc.identifier.urihttps://hdl.handle.net/20.500.12469/5124
dc.identifier.volume141en_US
dc.identifier.wosWOS:001000573300001en_US
dc.identifier.wosqualityQ1
dc.khas20231019-WoSen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofArtificial Intelligence in Medicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBlockchainen_US
dc.subjectChest CTen_US
dc.subjectCapsNetsen_US
dc.subjectProfileEn_Us
dc.subjectDeep Learningen_US
dc.subjectFederated Learningen_US
dc.subjectProfile
dc.subjectLung canceren_US
dc.titleA new lung cancer detection method based on the chest CT images using Federated Learning and blockchain systemsen_US
dc.typeArticleen_US
dspace.entity.typePublication

Files

Original bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
5124.pdf
Size:
2.57 MB
Format:
Adobe Portable Document Format
Description:
Tam Metin / Full Text