Abstract
The diagnosis of stroke, a leading cause of death in the world, using computed tomography (CT) scans, makes it possible to assess the severity of the incident and to determine 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 a more precise diagnosis of brain lesions caused by ischemic stroke events. To perform this task, we used a siamese network architecture that receives a double two-dimensional image of a CT slice (the original and a mirrored version) and a label that reflects the existence or not of a visible stroke event. The network then extracts the relevant features and can be used to classify brain-CT slices taking into account their perceived symmetry. The best performing network exhibits an average accuracy and F1-score of 72%, when applied to CT slices of previously unseen patients, significantly outperforming two state-of-the-art convolutional network architectures, which were used as baselines. When applied to slices chosen randomly, that may or may not be from the same patient, the network exhibits an accuracy of 97%, but this performance is due in part to overfitting, as the system is able to learn specific features of each patient brain.
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Notes
- 1.
Code available at: https://github.com/anagilvieira/siamese_network.git.
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Acknowledgments
This research was supported by the Portuguese Science Foundation, through the Projects PRELUNA - PTDC/CCI-INF/4703/2021 and UIDB/50021/2020.
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Vieira, A.B., Fonseca, A.C., Ferro, J., Oliveira, A.L. (2022). Using a Siamese Network to Accurately Detect Ischemic Stroke in Computed Tomography Scans. In: Marreiros, G., Martins, B., Paiva, A., Ribeiro, B., Sardinha, A. (eds) Progress in Artificial Intelligence. EPIA 2022. Lecture Notes in Computer Science(), vol 13566. Springer, Cham. https://doi.org/10.1007/978-3-031-16474-3_14
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