Fine Grained Neural Network Classification of Positron Emission Tomography Scans of Alzheimer’s and Normal Subjects

Samir Sayegh, Satoshi Minoshima, and David Kuhl

Positron Emission Tomography (PET) combined with a neural network for discrimination has shown promise for distinguishing patients with Alzheimer’s disease (AD) from normal patients (Kippnehahn et al, 1992; Kippenhahn et al., 1994, Chan et al. 1994, Page et al. 1996). Usually only a few parameters from the PET image are used, typically regions of inter est (ROI) data based on regional cerebral metabolic rates for glucose (rCMRglc) in multiple lobes. Such representation is highly compressed. Averaging or compression may eliminate a good deal of useful information. Advantages of such compact representation include convenience of usage of standard parameters, intuitive anatomical interpretation and fast training of the neural network. An alternative approach is to use the image as the network input pattern.

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