The Gaia Data Release 2 (DR2) provided an unprecedented volume of precise astrometric and excellent photometric data. In terms of data mining the Gaia catalogue, machine learning methods have shown to be a powerful tool, for instance in the search for unknown stellar structures. Particularly, supervised and unsupervised learning methods combined together significantly improves the detection rate of open clusters. We systematically scan Gaia DR2 in a region covering the Galactic anticentre and the Perseus arm (120{deg}<=l<=205{deg} and -10{deg}<=b<=10{deg}), with the goal of finding any open clusters that may exist in this region, and fine tuning a previously proposed methodology and successfully applied to TGAS data, adapting it to different density regions. Our methodology uses an unsupervised, density-based, clustering algorithm, DBSCAN, that identifies overdensities in the five-dimensional astrometric parameter space (l, b, {varpi}, pmRA*, pmDE) that may correspond to physical clusters. The overdensities are separated into physical clusters (open clusters) or random statistical clusters using an artificial neural network to recognise the isochrone pattern that open clusters show in a colour magnitude diagram. The method is able to recover more than 75% of the open clusters confirmed in the search area. Moreover, we detected 53 open clusters unknown previous to Gaia DR2, which represents an increase of more than 22% with respect to the already catalogued clusters in this region. We find that the census of nearby open clusters is not complete. Different machine learning methodologies for a blind search of open clusters are complementary to each other; no single method is able to detect 100% of the existing groups. Our methodology has shown to be a reliable tool for the automatic detection of open clusters, designed to be applied to the full Gaia DR2 catalogue.