Abstract
This paper offers a computer-aided diagnosis (CAD) technique for early diagnosis of Alzheimer’s disease (AD) by means of single photon emission computed tomography (SPECT) image classification. The SPECT database for different patients is analyzed by applying the Fisher discriminant ratio (FDR) and non-negative matrix factorization (NMF) for the selection and extraction of the most significative features of each patient SPECT data, in order to reduce the large dimensionality of the input data and the problem of the curse of dimensionality, extracting score features. The NMF-transformed set of data, with reduced number of features, is classified by means of support vector machines (SVM) classification. The proposed NMF+SVM method yields up to 94% classification accuracy, thus becoming an accurate method for SPECT image classification. For the sake of completeness, comparison between conventional PCA+SVM method and the proposed method is also provided.
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Padilla, P. et al. (2010). NMF-Based Analysis of SPECT Brain Images for the Diagnosis of Alzheimer’s Disease. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds) Hybrid Artificial Intelligence Systems. HAIS 2010. Lecture Notes in Computer Science(), vol 6076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13769-3_57
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DOI: https://doi.org/10.1007/978-3-642-13769-3_57
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