Using BERT models for breast cancer diagnosis from Turkish radiology reports.
Diagnostic radiology is concerned with obtaining images of the internal organs using radiological imaging procedures. These images are then interpreted by a diagnostic radiologist, who produces a textual report that assists in the diagnosis of illness or injury. Early detection of certain illnesses,...
| Publicado en: | Language Resources & Evaluation Vol. 58; no. 3; pp. 981 - 1013 |
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| Autores principales: | , , , |
| Formato: | Artículo |
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Springer Nature
Sep2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=179813860&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 179813860 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Sep2024 vid: 58 iid: 3 pid: 237 pub: Springer Nature artinfo: ui: 179813860 10.1007/s10579-023-09669-w ppf: 981 ppct: 32 formats: fmt: – @attributes: type: T – @attributes: type: P size: 2.2MB tig: atl: Using BERT models for breast cancer diagnosis from Turkish radiology reports. aug: au: Uskaner Hepsağ, Pınar Özel, Selma Ayşe Dalcı, Kubilay Yazıcı, Adnan affil: Department of Computer Engineering, Adana Alparslan Türkeş Science and Technology University, 01250, Adana, Turkey https://ror.org/05wxkj555 Department of Computer Engineering, Çukurova University, 01330, Adana, Turkey https://ror.org/05wxkj555 Department of General Surgery, Çukurova University, 01330, Adana, Turkey https://ror.org/052bx8q98 Department of Computer Science, Nazarbayev University, 010000, Nur Sultan, Kazakhstan su: Machine learning Language models Cancer diagnosis Early detection of cancer Breast cancer sug: subj: Machine learning Language models Cancer diagnosis Early detection of cancer Breast cancer keyword: Contextualized word embeddings Radiology reports Turkish dataset ab: Diagnostic radiology is concerned with obtaining images of the internal organs using radiological imaging procedures. These images are then interpreted by a diagnostic radiologist, who produces a textual report that assists in the diagnosis of illness or injury. Early detection of certain illnesses, particularly cancer, is critical, and the reports produced by diagnostic radiologists play a key role in this process. To develop models for the early detection of cancer, text classification techniques can be applied to radiological reports. However, this process requires access to a dataset of radiology reports, which is not widely available. Currently, radiology report datasets exist for high-resource languages such as English and Dutch, but not for low-resource languages such as Turkish. This article describes the collection of a mammography report dataset for Turkish, consisting of 62 reports from real patients that were manually labeled by an expert for diagnosing breast cancer. Basic machine learning models were applied to this dataset using pre-trained BERT, DistilBERT, and an ensemble learning hard voting approach. The results showed that BERT on Turkish achieved the best performance, with a 91% F1-score. Hard Voting, which combined the results of BERT, BERT, and BERT, achieved the highest F1-score of 93%. The results show that BERT and Hard Voting outperform the other machine learning models for breast cancer diagnosis from Turkish radiology reports. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2024. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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