Automatic Identification of Intracranial Hemorrhage on CT/MRI Image Using Meta-Architectures Improved from Region-Based CNN

  • Thi-Hoang-Yen LeEmail author
  • Anh-Cang Phan
  • Hung-Phi Cao
  • Thuong-Cang Phan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 991)


Machine learning algorithms are suggested for detecting and classifying hemorrhage regions on head CT/MRI images with high accuracy. However, most of these algorithms are not interested in the valuable characteristic of CT/MRI images, especially Hounsfield Unit values. Besides, they also only detect and classify one of types of the intracranial hemorrhage on each image. In this paper, we propose a new approach for brain hemorrhage identification using object detection algorithms like Faster R-CNN and R-FCN. The proposed approach can detect many regions of the brain hemorrhage on a CT image. The results show that the R-FCN algorithm gives better results than the Faster R-CNN algorithm on time and accuracy of identification.


Intracranial hemorrhage Region-based CNN Meta-architectures Hounsfield unit 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Thi-Hoang-Yen Le
    • 1
    Email author
  • Anh-Cang Phan
    • 1
  • Hung-Phi Cao
    • 1
  • Thuong-Cang Phan
    • 2
  1. 1.Vinh Long University of Technology EducationVinh LongVietnam
  2. 2.Can Tho UniversityCan ThoVietnam

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