Deep Learning-Based Brain Hemorrhage Detection in CT Reports

Creative Commons License

Bayrak G., Toprak M. S., GANİZ M. C., Kodaz H., Koç U.

32nd Medical Informatics Europe Conference, MIE 2022, Nice, Fransa, 27 - 30 Mayıs 2022, cilt.294, ss.866-867 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 294
  • Doi Numarası: 10.3233/shti220609
  • Basıldığı Şehir: Nice
  • Basıldığı Ülke: Fransa
  • Sayfa Sayıları: ss.866-867
  • Anahtar Kelimeler: Brain Hemorrhage, Deep Learning, NLP, Radiology
  • Marmara Üniversitesi Adresli: Evet

Özet

© 2022 European Federation for Medical Informatics (EFMI) and IOS Press.Radiology reports can potentially be used to detect critical cases that need immediate attention from physicians. We focus on detecting Brain Hemorrhage from Computed Tomography (CT) reports. We train a deep learning classifier and observe the effect of using different pre-trained word representations along with domain-specific fine-tuning. We have several contributions. Firstly, we report the results of a large-scale classification model for brain hemorrhage detection from Turkish radiology reports. Second, we show the effect of fine-tuning pre-trained language models using domain-specific data on the performance. We conclude that deep learning models can be used for detecting brain Hemorrhage with reasonable accuracy and fine-tuning language models using domain-specific data to improve classification performance.