Using BERT models for breast cancer diagnosis from Turkish radiology reports

Pınar Uskaner Hepsağ, Selma Ayşe Özel, Kubilay Dalcı, Adnan Yazıcı

Research output: Contribution to journalArticlepeer-review

Abstract

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 BERTTurkish, BERTClinical, and BERTMultilingual, 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.

Original languageEnglish
JournalLanguage Resources and Evaluation
DOIs
Publication statusAccepted/In press - 2023

Keywords

  • Breast cancer
  • Contextualized word embeddings
  • Machine learning
  • Radiology reports
  • Turkish dataset

ASJC Scopus subject areas

  • Language and Linguistics
  • Education
  • Linguistics and Language
  • Library and Information Sciences

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