Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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Diagnosis and Visualization of Intracranial Hemorrhage on Computed Tomography Images Using EfficientNet-based Model

전산화 단층 촬영(Computed tomography, CT) 이미지에 대한 EfficientNet 기반 두개내출혈 진단 및 가시화 모델 개발

  • Youn, Yebin (Korea Brain Research Institute, Cognitive Science Group, Deep Memory Lab) ;
  • Kim, Mingeon (Siemens Healthineers Ltd. Diagnostic Imaging) ;
  • Kim, Jiho (Korea Brain Research Institute, Cognitive Science Group, Deep Memory Lab) ;
  • Kang, Bongkeun (Daegu-Gyeongbuk Medical Innovation Foundation, Medical Device Development Center) ;
  • Kim, Ghootae (Korea Brain Research Institute, Cognitive Science Group, Deep Memory Lab)
  • Received : 2021.03.05
  • Accepted : 2021.07.13
  • Published : 2021.08.31
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Abstract

Intracranial hemorrhage (ICH) refers to acute bleeding inside the intracranial vault. Not only does this devastating disease record a very high mortality rate, but it can also cause serious chronic impairment of sensory, motor, and cognitive functions. Therefore, a prompt and professional diagnosis of the disease is highly critical. Noninvasive brain imaging data are essential for clinicians to efficiently diagnose the locus of brain lesion, volume of bleeding, and subsequent cortical damage, and to take clinical interventions. In particular, computed tomography (CT) images are used most often for the diagnosis of ICH. In order to diagnose ICH through CT images, not only medical specialists with a sufficient number of diagnosis experiences are required, but even when this condition is met, there are many cases where bleeding cannot be successfully detected due to factors such as low signal ratio and artifacts of the image itself. In addition, discrepancies between interpretations or even misinterpretations might exist causing critical clinical consequences. To resolve these clinical problems, we developed a diagnostic model predicting intracranial bleeding and its subtypes (intraparenchymal, intraventricular, subarachnoid, subdural, and epidural) by applying deep learning algorithms to CT images. We also constructed a visualization tool highlighting important regions in a CT image for predicting ICH. Specifically, 1) 27,758 CT brain images from RSNA were pre-processed to minimize the computational load. 2) Three different CNN-based models (ResNet, EfficientNet-B2, and EfficientNet-B7) were trained based on a training image data set. 3) Diagnosis performance of each of the three models was evaluated based on an independent test image data set: As a result of the model comparison, EfficientNet-B7's performance (classification accuracy = 91%) was a way greater than the other models. 4) Finally, based on the result of EfficientNet-B7, we visualized the lesions of internal bleeding using the Grad-CAM. Our research suggests that artificial intelligence-based diagnostic systems can help diagnose and treat brain diseases resolving various problems in clinical situations.

Keywords

Acknowledgement

본 연구는 과학기술정보통신부의 재원으로 한국뇌연구원의 KBRI 기초 연구 프로그램의 지원을 받아 수행된 연구임(21-BR-03-04).

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