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Deep Learning in the Detection of Rare Fractures – Development of a “Deep Learning Convolutional Network” Model for Detecting Acetabular Fractures
7
Zitationen
7
Autoren
2021
Jahr
Abstract
The accuracy of fracture detection of our trained DCNN is comparable to published values despite the low number of training datasets. The techniques of bone extraction, DA and GAP are useful for increasing the detection rates of rare fractures by a DCNN. Based on the used DCNN in combination with the described techniques from this pilot study, the possibility of an automatic fracture classification of AF is under investigation in a multicentre study.
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