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Machine learning techniques have been increasingly used in astronomicalapplications and have proven to successfully classify objects in image datawith high accuracy. The current work uses archival data from the Faint Imagesof the Radio Sky at Twenty Centimeters (FIRST) to classify radio galaxies intofour classes: Fanaroff-Riley Class I (FRI), Fanaroff-Riley Class II (FRII),Bent-Tailed (BENT), and Compact (COMPT). The model presented in this work isbased on Convolutional Neural Networks (CNNs). The proposed architecturecomprises three parallel blocks of convolutional layers combined and processedfor final classification by two feed-forward layers. Our model classifiedselected classes of radio galaxy sources on an independent testing subset withan average of 96\% for precision, recall, and F1 score. The best selectedaugmentation techniques were rotations, horizontal or vertical flips, andincrease of brightness. Shifts, zoom and decrease of brightness worsened theperformance of the model. The current results show that model developed in thiswork is able to identify different morphological classes of radio galaxies witha high efficiency and performance

Morphological classification of compact and extended radio galaxies using convolutional neural networks and data augmentation techniques