The development of methods based on artificial intelligence for the classification
of medical imaging is widespread. Given the high dimensionality of this type
of images, it is imperative to use the information contained in relevant regions
for further classification. This information can be derived from the morphology of the region of interest, in terms of measurements such as area, perimeter,
etc. However, the performance of the classification system strongly depends on
the correct selection of the type of information employed. We propose in this
work an alternative for evaluating differences between brain regions that relies
on the basis of Siamese neural networks. Initially, brain scans are delimited by
an anatomical atlas. Next, each pair of regions of interest is then entered into
a Siamese network, which is formed by relating the distance between the two
individual outputs and the corresponding label. Features are extracted from
the embeddings of the final linear layer. Finally, the classification is performed
by combining the characteristics of each pair of regions into an ensemble architecture. Performance was assessed by determining how asymmetry between the
right and left hemispheres changes during progressive brain degeneration, from
mild cognitive impairment to severe atrophy associated with Alzheimer’s disease
(AD). Our method discriminates with an accuracy of 98.95% between controls
and AD patients, and most important, it predicts the cognitive decline in patients suffering from mild cognitive impairment that will develop AD before it
occurs with an accuracy of 78.41%. These results demonstrate the applicability
of our proposal in the study of a wide range of pathologies.
