We consider the problem of the detection of brain hemorrhages from three
dimensional (3D) electrical impedance tomography (EIT) measurements. This is a
condition requiring urgent treatment for which EIT might provide a portable and
quick diagnosis. We employ two neural network architectures -- a fully
connected and a convolutional one -- for the classification of hemorrhagic and
ischemic strokes. The networks are trained on a dataset with 40000 samples
of synthetic electrode measurements generated with the complete electrode model
on realistic heads with a 3-layer structure. We consider changes in head
anatomy and layers, electrode position, measurement noise and conductivity
values. We then test the networks on several datasets of unseen EIT data, with
more complex stroke modeling (different shapes and volumes), higher levels of
noise and different amounts of electrode misplacement. On most test datasets we
achieve ≥90% average accuracy with fully connected neural networks,
while the convolutional ones display an average accuracy ≥80%. Despite
the use of simple neural network architectures, the results obtained are very
promising and motivate the applications of EIT-based classification methods on
real phantoms and ultimately on human patients.Comment: 17 pages, 11 figure