Fig 7 -

Fig 7 -

<p><b>(A)</b> ROC curves show relative performance of each non-ANN machine learning method, <b>(B)</b> Aggregated performance ROC curve for ANN with 35 training epochs. Area Under the Curve (AUC) metrics are provided in the figure legends to demonstrate interaction of f...

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Bibliographic Details
Main Author: Ashutosh P. Raman (19825936) (author)
Other Authors: Tanner J. Zachem (19825939) (author), Sarah Plumlee (19825942) (author), Christine Park (10261868) (author), William Eward (19825945) (author), Patrick J. Codd (14979591) (author), Weston Ross (19825948) (author)
Published: 2024
Subjects:
Medicine
Biotechnology
Cancer
Space Science
Biological Sciences not elsewhere classified
Chemical Sciences not elsewhere classified
Information Systems not elsewhere classified
use machine learning
support vector machines
support vector machine
machine learning approaches
involve many challenges
following study builds
first known study
current surgical instrumentation
box ann architecture
biomedical sensing landscape
based spectroscopic signatures
automate photonic diagnosis
tissue healing difficulty
implementing classification algorithms
interpretable algorithms
classification accuracies
normal tissue
ambiguous tissue
utilizes autofluorescence
tumor boundary
substantial research
standard risks
previously devised
precise determination
nearest neighbors
logistic regression
interpret data
direct applications
curve scores
critical need
clear way
care platform
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