Abstract
This paper presents a computer aided diagnosis technique for improving the accuracy of the early diagnosis of the Alzheimer’s disease. 97 SPECT brain images from the “Virgen de las Nieves” Hospital in Granada are studied. The proposed method is based on two different classifiers that use two different separability criteria and a posterior reduction of the feature dimension using factor analysis. Factor loadings are used as features of two multivariate classifiers with quadratic discriminant functions. The result of these two different classifiers is used to figure out the final decision. An accuracy rate up to 92.78 % when NC and AD are considered is obtained using the proposed methodology.
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Martínez, F.J., Salas-González, D., Górriz, J.M., Ramírez, J., Puntonet, C.G., Gómez-Río, M. (2011). Analysis of Spect Brain Images Using Wilcoxon and Relative Entropy Criteria and Quadratic Multivariate Classifiers for the Diagnosis of Alzheimer’s Disease. In: Ferrández, J.M., Álvarez Sánchez, J.R., de la Paz, F., Toledo, F.J. (eds) New Challenges on Bioinspired Applications. IWINAC 2011. Lecture Notes in Computer Science, vol 6687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21326-7_5
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DOI: https://doi.org/10.1007/978-3-642-21326-7_5
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