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Context. The accurate classification of hundreds of thousands of galaxiesobserved in modern deep surveys is imperative if we want to understand theuniverse and its evolution. Aims. Here, we report the use of machine learningtechniques to classify early- and late-type galaxies in the OTELO and COSMOSdatabases using optical and infrared photometry and available shape parameters:either the Sersic index or the concentration index. Methods. We used threeclassification methods for the OTELO database: 1) u-r color separation , 2)linear discriminant analysis using u-r and a shape parameter classification,and 3) a deep neural network using the r magnitude, several colors, and a shapeparameter. We analyzed the performance of each method by sample bootstrappingand tested the performance of our neural network architecture using COSMOSdata. Results. The accuracy achieved by the deep neural network is greater thanthat of the other classification methods, and it can also operate with missingdata. Our neural network architecture is able to classify both OTELO and COSMOSdatasets regardless of small differences in the photometric bands used in eachcatalog. Conclusions. In this study we show that the use of deep neuralnetworks is a robust method to mine the cataloged data

Galaxy classification: deep learning on the OTELO and COSMOS databases