Automated classification reveals morphological factors associated with dementia
Applied Soft Computing
Automated classification of dementia subtypes from post-mortem cortex images
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
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Like many diseases, dementia is associated with a changed physical structure of diseased tissue. This study is a preliminary attempt to show that these changes are detectable using image processing, and could facilitate the automated classification of dementia subtypes. The identification of a link between different pathologies and the physical structure of tissue is potentially of great benefit to our understanding of this group of diseases. We have shown the existence of such a link by applying machine learning techniques to features derived using fractal analysis, as well as classical shape parameters.Automated classification is a common goal of machine learning, and consists of assigning a class label to a set of measurements. Classification of unlabelled samples is preceded by a learning phase, where labeled samples are presented, and the relationship between measurements and class label is determined. A variety of statistical and machine learning methods are applicable to this kind of problem, but rely on the availability of a suitable set of measurements comprising a feature vector.