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Convolutional neural networks (CNNs) are the state-of-the-art technique foridentifying strong gravitational lenses. Although they are highly successful inrecovering genuine lens systems with a high true-positive rate, the unbalancednature of the data set (lens systems are rare), still leads to a high falsepositive rate. For these techniques to be successful in upcoming surveys (e.g.with Euclid) most emphasis should be set on reducing false positives, ratherthan on reducing false negatives. In this paper, we introduce densely connectedneural networks (DenseNets) as the CNN architecture in a new pipeline-ensemblemodel containing an ensemble of classification CNNs and regression CNNs toclassify and rank-order lenses, respectively. We show that DenseNets achievecomparable true positive rates but considerably lower false positive rates(when compared to residual networks; ResNets). Thus, we recommend DenseNets forfuture missions involving large data sets, such as Euclid, where low falsepositive rates play a key role in the automated follow-up and analysis of largenumbers of strong gravitational lens candidates when human vetting is no longerfeasible

DenseLens -- Using DenseNet ensembles and information criteria for finding and rank-ordering strong gravitational lenses,