In Studies in health technology and informatics ; h5-index 23.0
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.
Danilov Gleb, Kotik Konstantin, Negreeva Anna, Tsukanova Tatiana, Shifrin Michael, Zakharova Natalya, Batalov Artem, Pronin Igor, Potapov Alexander
Intracranial hemorrhage, computed tomography, deep learning, neurosurgery