Abstract
Alzheimer disease (AD) is a progressive neurodegenerative disorder first affecting memory functions and then gradually affecting all cognitive functions with behavioral impairments. As the number of AD patients has increased, early diagnosis has received more attention for both social and medical reasons. Currently, accuracy in the early AD diagnosis is below 70% so that AD does not receive a suitable treatment. Functional brain imaging including single-photon emission computed tomography (SPECT) is commonly used to guide the clinician’s diagnosis. However, conventional evaluation of SPECT scans often relies on manual reorientation, visual reading and semiquantitative analysis of certain regions of the brain. This paper evaluates different pattern classifiers for the development of a computer aided diagnosis (CAD) system for improving the early AD detection. Discriminant template-based normalized mean square error (NMSE) features of several coronal slices of interest (SOI) were used. The proposed system yielding a 97% AD diagnosis accuracy, reports clear improvements over existing techniques such as the voxel-as-features (VAF) which yields just a 78% classification accuracy.
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Ramírez, J. et al. (2009). SPECT Image Classification Techniques for Computer Aided Diagnosis of the Alzheimer Disease. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds) Bio-Inspired Systems: Computational and Ambient Intelligence. IWANN 2009. Lecture Notes in Computer Science, vol 5517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02478-8_118
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DOI: https://doi.org/10.1007/978-3-642-02478-8_118
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