of them based on the probability that they contain a
tumor. Using the detection metrics, the performance
of the network was measured with the remaining 15%
of the dataset in order to evaluate its robustness. After
training the network, the results show that this pro-
posed computer-aided diagnosis method achieved a
mean accuracy of 97.375% proving that the system
could aid specialized doctors to recognize cancerous
signs when analyzing mammograms, improving pa-
tients’ quality of life.
In future works, the authors will study different
CNNs models and also other network architectures
like Mask R-CNNs instead of Faster R-CNNs in order
to not only locate the tumor inside the mammogram,
but also to create a mask with its shape. This way, the
size of the tumor can be estimated more precisely and
taken into account in the decision making task.
ACKNOWLEDGEMENTS
This work was supported by the excellence project
from the Spanish government grant (with support
from the European Regional Development Fund)
COFNET (TEC2016-77785-P).
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