  We present a novel machine learning based approach for detecting galaxy-scale
gravitational lenses from interferometric data, specifically those taken with
the International LOFAR Telescope (ILT), which is observing the northern radio
sky at a frequency of 150 MHz, an angular resolution of 350 mas and a
sensitivity of 90 uJy beam-1 (1 sigma). We develop and test several
Convolutional Neural Networks to determine the probability and uncertainty of a
given sample being classified as a lensed or non-lensed event. By training and
testing on a simulated interferometric imaging data set that includes realistic
lensed and non-lensed radio sources, we find that it is possible to recover
95.3 per cent of the lensed samples (true positive rate), with a contamination
of just 0.008 per cent from non-lensed samples (false positive rate). Taking
the expected lensing probability into account results in a predicted sample
purity for lensed events of 92.2 per cent. We find that the network structure
is most robust when the maximum image separation between the lensed images is
greater than 3 times the synthesized beam size, and the lensed images have a
total flux density that is equivalent to at least a 20 sigma (point-source)
detection. For the ILT, this corresponds to a lens sample with Einstein radii
greater than 0.5 arcsec and a radio source population with 150 MHz flux
densities more than 2 mJy. By applying these criteria and our lens detection
algorithm we expect to discover the vast majority of galaxy-scale gravitational
lens systems contained within the LOFAR Two Metre Sky Survey.
