Classification of Intracranial Hemorrhage Subtypes Using Deep Learning on CT Scans.

Journal: Studies in health technology and informatics
Published Date: Jun 26, 2020

Abstract

Intracranial hemorrhage is a pathological condition that requires fast diagnosis and decision making. Recently, a neural network model for classification of different intracranial hemorrhage types was proposed by a member of our research group Konstantin Kotik as part of the machine learning competition at Kaggle. Our current pilot study aimed to test this model on real-world CT scans from patients with intracranial hemorrhage treated at N.N. Burdenko Neurosurgery Center. The deep learning model for intracranial hemorrhage classification based on ResNexT architecture showed an accuracy of detection greater than 0.81 for every subtype of hemorrhage without any tuning. We expect further improvement in the model performance.

Authors

  • Gleb Danilov
    Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation.
  • Konstantin Kotik
    Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation.
  • Anna Negreeva
    Department of neuroradiology, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation.
  • Tatiana Tsukanova
    Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation.
  • Michael Shifrin
    Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation.
  • Natalya Zakharova
    Department of neuroradiology, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation.
  • Artem Batalov
    Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation.
  • Igor Pronin
    Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation.
  • Alexander Potapov
    Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation.

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