Abstract
After traumatic brain injury (TBI), clinicians use the Glasgow Coma Scale (GCS) to classify patients by severity and radiologists use the Rotterdam score and the Marshall score to classify CT scans by severity. This work investigates a viable efficient low-cost transfer learning model to use MRI images to predict GCS, Rotterdam, and Marshall severity class after TBI. The enhanced transfer learning model architecture integrates multiple fine-tuning steps in which a few layers of the pre-trained model is unfrozen in each iteration so that the neural network is able to learn layer by layer further aspects of the image for better classification. By conducting a thorough evaluation across multiple convolutional neural network (CNN) architectures, this work ascertains the sensitivity of CNN models in detecting anatomical changes presented in MRI images that are predictive of severity class after TBI. These models have potential to predict outcomes after TBI. We utilize both quantitative metrics and qualitative analysis to validate the clinical relevance of the models in predicting severity class on admission and outcome at 6 months. The residual network models (ResNet152 in particular) outperformed the other models in predicting initial severity class.
Recommended Citation
N. Do et al., "Evaluation of Transfer Learning Models on Traumatic Brain Injury Severity Classification," 21st IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2024, Institute of Electrical and Electronics Engineers, Jan 2024.
The definitive version is available at https://doi.org/10.1109/CIBCB58642.2024.10702135
Department(s)
Electrical and Computer Engineering
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2024
