Patra Charalampaki*, Irini Kakaletri and Stamatia Giannarou
Volume3-Issue12
Dates: Received: 2022-12-18 | Accepted: 2022-12-24 | Published: 2022-12-26
Pages: 1527-1531
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.
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DOI: 10.37871/jbres1629
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Copyright
© 2022 Charalampaki P, et al. Distributed under Creative Commons CC-BY 4.0 
How to cite this article
Charalampaki P, Kakaletri I, Giannarou S. Can Endomicroscopic Tissue Characterization be Combined with Deep Machine Learning to Support Neurosurgery? 2022 Dec 26; 3(12): 1527-1531. doi: 10.37871/jbres1629, Article ID: JBRES1629, Available at: https://www.jelsciences.com/articles/jbres1629.pdf
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