An efficient deep neural network for automatic classification of acute intracranial hemorrhages in brain CT scans

Yu Ruei Chen, Chih Chieh Chen, Chang Fu Kuo, Ching Heng Lin*

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

1 Scopus citations

Abstract

Background: Recent advancements in deep learning models have demonstrated their potential in the field of medical imaging, achieving remarkable performance surpassing human capabilities in tasks such as classification and segmentation. However, these modern state-of-the-art network architectures often demand substantial computational resources, which limits their practical application in resource-constrained settings. This study aims to propose an efficient diagnostic deep learning model specifically designed for the classification of intracranial hemorrhage in brain CT scans. Method: Our proposed model utilizes a combination of depthwise separable convolutions and a multi-receptive field mechanism to achieve a trade-off between performance and computational efficiency. The model was trained on RSNA datasets and validated on CQ500 dataset and PhysioNet dataset. Result: Through a comprehensive comparison with state-of-the-art models, our model achieves an average AUROC score of 0.952 on RSNA datasets and exhibits robust generalization capabilities, comparable to SE-ResNeXt, across other open datasets. Furthermore, the parameter count of our model is just 3 % of that of MobileNet V3. Conclusion: This study presents a diagnostic deep-learning model that is optimized for classifying intracranial hemorrhages in brain CT scans. The efficient characteristics make our proposed model highly promising for broader applications in medical settings.

Original languageEnglish
Article number108587
Pages (from-to)108587
JournalComputers in Biology and Medicine
Volume176
DOIs
StatePublished - 06 2024
Externally publishedYes

Bibliographical note

Copyright © 2024 Elsevier Ltd. All rights reserved.

Keywords

  • Deep learning
  • Efficient neural network
  • Intracranial hemorrhage
  • Multi-receptive field
  • Brain/diagnostic imaging
  • Neural Networks, Computer
  • Tomography, X-Ray Computed/methods
  • Humans
  • Intracranial Hemorrhages/diagnostic imaging
  • Deep Learning
  • Databases, Factual

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