Abstract
Confocal Laser Endomicroscopy (CLE) is a new technique that is able to show cell structures during surgery. The interpretation of CLE data for tissue characterisation during brain tumour resection is challenging even among experts and it can lead to considerable inter-observer variability. Different kinds of deep machine learning programs and models were developed for better interpretation of the cell findings. A few-shot learning framework is proposed to assess the diagnostic value of CLE data and to classify them into healthy tissue and different brain tumour types, namely glioblastoma, meningioma, or astrocytoma. Performance evaluation on ex vivo and in vivo data shows that the rejection of data with low diagnostic value improves the classification accuracy by 37.5% while the proposed tissue characterisation framework achieves 96.20% classification accuracy.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Journal of Biomedical Research & Environmental Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.