Evaluation of techniques to improve a deep learning algorithm for the automatic detection of intracranial haemorrhage on CT head imaging

Evaluation of techniques to improve a deep learning algorithm for the automatic detection of intracranial haemorrhage on CT head imaging

Abstract Background Deep learning (DL) algorithms are playing an increasing role in automatic medical image analysis. Purpose To evaluate the performance of a DL model for the automatic detection of intracranial haemorrhage and its subtypes on non-contrast CT (NCCT) head studies and to compare the e...

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Bibliographic Details
Main Authors: Melissa Yeo, Bahman Tahayori, Hong Kuan Kok, Julian Maingard, Numan Kutaiba, Jeremy Russell, Vincent Thijs, Ashu Jhamb, Ronil V. Chandra, Mark Brooks, Christen D. Barras, Hamed Asadi
Format: Article
Language:English
Published: SpringerOpen 2023-04-01
Series:European Radiology Experimental
Subjects:
Artificial intelligence
Deep learning
Intracranial haemorrhages
Radiographic image interpretation (computer-assisted)
Tomography (x-ray computed)
Online Access:https://doi.org/10.1186/s41747-023-00330-3

Internet

https://doi.org/10.1186/s41747-023-00330-3

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