Automated medical image processing, particularly of radiological images, can reduce the number of diagnostic errors, increase patient care and reduce medical costs. This paper seeks to evaluate the performance of three recent convolutional neural networks in the autonomous identification of fissures over two-dimensional radiological images. These architectures have been proposed as deep neural network types specially designed for image classification, which allows their integration with traditional image processing strategies for automatic analysis of medical images. In particular, we use three convolutional networks: ResNet (residual neural network), DenseNet (dense convolutional network), and NASNet (neural architecture search network) to learn information from a set of 200 images labeled half as fissured bones and half as seamless bones. All three networks are trained and adjusted under the same conditions, and their performance was evaluated with the same metrics. The final results consider not only the model's ability to predict the characteristics of an unknown image but also its internal complexity. The three neural models were optimized to reduce classification errors without producing network over-adjustment. In all three cases, generalization of behavior was observed, and the ability of the models to identify the images with fissures, however the expected performance was only achieved with the NASNet model.