Deep learning for behaviour classification in a preclinical brain injury model.

ABSTRACT: The early detection of traumatic brain injuries can directly impact the prognosis and survival of patients. Preceding attempts to automate the detection and the assessment of the severity of traumatic brain injury continue to be based on clinical diagnostic methods, with limited tools for disease outcomes in large populations. Despite advances in machine and deep learning tools, current approaches still use simple trends of statistical analysis which lack generality. The effectiveness of deep learning to extract information from large subsets of data can be further emphasised through the use of more elaborate architectures. We therefore explore the use of a multiple input, convolutional neural network and long short-term memory (LSTM) integrated architecture in the context of traumatic inj

SUBMITTER: Teoh L 

PROVIDER: S-EPMC9200342 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature