Deep learning-based brain hemorrhage detection in CT reports - GANİZ, MURAT CAN (My University, 2022)
© 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.
Keywords: Tıp; Dahili Tıp Bilimleri; Aile Hekimliği; Biyomedikal Mühendisliği; Sağlık Bilimleri; Temel Tıp Bilimleri; Biyoistatistik ve Tıp Bilişimi; Mühendislik ve Teknoloji; Medicine; Internal Medicine Sciences; Family Medicine; Biomedical Engineering; Health Sciences; Fundamental Medical Sciences; Biostatistics and Medical Informatics; Engineering and Technology; Klinik Tıp (MED); Mühendislik, Bilişim ve Teknoloji (ENG); Klinik Tıp; Mühendislik; TIBBİ BİLİŞİM; SAĞLIK BAKIM BİLİMLERİ VE HİZMETLERİ; MÜHENDİSLİK, BİYOMEDİKAL; Clinical Medicine (MED); Engineering, Computing & Technology (ENG); CLINICAL MEDICINE; ENGINEERING; MEDICAL INFORMATICS; HEALTH CARE SCIENCES & SERVICES; ENGINEERING, BIOMEDICAL; Biyomedikal mühendisliği; Fizik Bilimleri; Tıbbi Bilişim; Sağlık Bilgi Yönetimi; Physical Sciences; Health Informatics; Health Information Management; Brain Hemorrhage; Deep Learning; NLP; Radiology