Abstract: The present invention relates to a method and a system for identifying an anomaly in a chest X-ray image in posteroanterior orientation based on neural networks, which comprises: receiving the image in a classification module with a plurality of convolutional neural networks, each trained to identify at least one specific graphic pattern associated with a pathology; obtaining, for each of the convolutional neural networks, a probability of detecting the specific graphic pattern; receiving in a fully connected neural network all the probabilities obtained; and determining whether the chest X-ray image contains an anomaly, based on the detection probabilities provided.

Abstract: The present invention relates to a method and a system for the segmentation of white matter hyperintensities (WMHs) present in magnetic resonance brain images, comprising: providing an array of trained convolutional neural networks (CNNs) with a magnetic resonance brain image; determining, for each of the CNNs and for each voxel, the probability that the given voxel corresponds to a pathological hyperintensity; calculating the average of all the probabilities determined for each voxel; comparing the averaged probabilities for each voxel with a threshold; generating an image mask with the voxels that exceed the threshold.