Using a Siamese Network to Accurately Detect Ischemic Stroke in Computed Tomography Scans

The diagnosis procedure of stroke, a leading cause of death in the world, involves the acquisition of images using computed tomography scans, making possible the assessment of the severity of the incident and the type and location of the lesion. The fact that the brain has two hemispheres with a high level of anatomical similarity, exhibiting significant symmetry, has led to extensive research based on the assumption that a decrease in symmetry is directly related to the presence of pathologies. This work is focused on the analysis of the symmetry (or lack of it) of the two brain hemispheres, and on the use of this information for the classification of computed tomography brain scans of stroke patients. The objective is to contribute to the process of automatic identification of brain lesions caused by stroke events. To perform this task, we used the Siamese Network architecture, which uses two parallel neural networks that share the same weights. The composed network receives a double image (the original image and the mirrored one) and a label that reflects the existence or not of stroke. The network then extracts the relevant features and classifies the images taking into account their similarity. The resulting network can be used to classify unseen scans, depending on the perceived level of symmetry into one of two existing classes: evidence of stroke or absence of stroke. The accuracy of the proposed method is approximately 72%, significantly outperforming a standard convolutional network architecture, which was used as a baseline.