Yayın: Big data analytics and radiomics to discover diagnostics on different cancer types
| dc.contributor.author | Bayrak, Ebru Aydındağ | |
| dc.contributor.author | Kırcı, Pınar | |
| dc.contributor.buuauthor | KIRCI, PINAR | |
| dc.contributor.department | Bursa Uludağ Üniversitesi | |
| dc.contributor.scopusid | 15026635000 | |
| dc.date.accessioned | 2025-05-13T06:41:28Z | |
| dc.date.issued | 2022-01-01 | |
| dc.description.abstract | This chapter presents a brief introduction to the usage areas of big data analytics and radiomics on cancer diagnosis in the healthcare system. The diagnostics on different cancer types based on big data analytics and radiomics are focused in this study. Big data analytics and radiomics have been introduced. And how they are used for the early diagnosis and prediction of several cancer types are explained. A great number of academic studies based on the detection of cancer using both big data analytics and radiomics have been consistently reviewed and worked on by researchers. Furthermore, the applications of use with radiomics and big data analytics in healthcare have been investigated. The definition of radiomics and big data, the usage areas of radiomics and big data analytics, similarities, differences, challenges of radiomics and big data analytics, and the general information are mentioned. The main aim of the study is helping researchers to easily enable an early diagnosis and prediction of different cancer types using big data analytics and radiomics. Big data analytics and radiomics are especially used in the healthcare system to obtain useful information about early diagnosis of any potential for various cancer diseases, the survivability rate from cancer, and the rate of propagation of existing cancer. | |
| dc.identifier.doi | 10.1016/B978-0-323-91907-4.00016-9 | |
| dc.identifier.endpage | 138 | |
| dc.identifier.isbn | [9780323919074, 9780323985161] | |
| dc.identifier.scopus | 2-s2.0-85137586734 | |
| dc.identifier.startpage | 125 | |
| dc.identifier.uri | https://hdl.handle.net/11452/51747 | |
| dc.indexed.scopus | Scopus | |
| dc.language.iso | en | |
| dc.publisher | Elsevier | |
| dc.relation.journal | Big Data Analytics for Healthcare: Datasets, Techniques, Life Cycles, Management, and Applications | |
| dc.rights | info:eu-repo/semantics/closedAccess | |
| dc.subject | Radiomics analysis | |
| dc.subject | Radiomics | |
| dc.subject | Cancer | |
| dc.subject | Big data analytics | |
| dc.subject | Big data | |
| dc.subject.scopus | Radiomics; Computer Assisted Tomography; Magnetic Resonance Imaging | |
| dc.title | Big data analytics and radiomics to discover diagnostics on different cancer types | |
| dc.type | Book Chapter | |
| dspace.entity.type | Publication | |
| local.contributor.department | Bursa Uludağ Üniversitesi | |
| local.indexed.at | Scopus | |
| relation.isAuthorOfPublication | 0270c3e7-f379-4f0e-84dd-a83c2bbf0235 | |
| relation.isAuthorOfPublication.latestForDiscovery | 0270c3e7-f379-4f0e-84dd-a83c2bbf0235 |
