Fig 7 -

(A) ROC curves show relative performance of each non-ANN machine learning method, (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...

وصف كامل

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Ashutosh P. Raman (19825936) (author)
مؤلفون آخرون:	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)
منشور في:	2024
الموضوعات:	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
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

الوصف
الملخص:	<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 false and true positive rates.</p>

Fig 7 -

مواد مشابهة