Application of fuzzy logic for Alzheimer's disease diagnosis

2015 Signal Processing Symposium, SPSympo 2015

Volumn , Issue , 2015, Pages

  Krashenyi, Igor ^a   Popov, Anton ^a   Ramirez, Javier ^b   Gorriz, Juan Manuel ^b  

^a NATIONAL TECHNICAL UNIVERSITY OF UKRAINE   (Ukraine)

^b UNIVERSITY OF GRANADA   (Spain)

Author keywords

Alzheimer's disease; classification; fuzzy logic; mild cognitive impairment; MRI

Indexed keywords

BRAIN; CLASSIFICATION (OF INFORMATION); CLUSTERING ALGORITHMS; COMPUTER CIRCUITS; DIAGNOSIS; FUZZY INFERENCE; INFERENCE ENGINES; MAGNETIC RESONANCE IMAGING; NEURODEGENERATIVE DISEASES;

ALZHEIMER'S DISEASE; FUZZY INFERENCE SYSTEMS; K FOLD CROSS VALIDATIONS; MILD COGNITIVE IMPAIRMENTS; MILD COGNITIVE IMPAIRMENTS (MCI); POSITIVE PREDICTIVE VALUES; STANDARD DEVIATION; SUBTRACTIVE CLUSTERING ALGORITHMS;

FUZZY LOGIC;

EID: 84945972057     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SPS.2015.7168288     Document Type: Conference Paper

References (23)

1. Mayeux, R. (2010). Early Alzheimer's disease. New England Journal of Medicine 362, 2194-2201.
2. Vemuri, P., and Jack Jr, C.R. (2010). Role of structural MRI in Alzheimer's disease. Alzheimers Res Ther 2, 23.
3. Duchesne, S., Caroli, A., Geroldi, C., Barillot, C., Frisoni, G.B., and Collins, D.L. (2008). MRI-Based Automated Computer Classification of Probable AD Versus Normal Controls. IEEE Transactions on Medical Imaging 27, 509-520.
4. Álvarez, I., Górriz, J.M., Ramírez, J., Salas-Gonzalez, D., López, M., Segovia, F., Puntonet, C.G., and Prieto, B. (2009). Alzheimer's diagnosis using eigenbrains and support vector machines. In Bio-Inspired Systems: Computational and Ambient Intelligence, (Springer), pp. 973-980.
5. Górriz, J.M., Ramírez, J., Lassl, A., Salas-Gonzalez, D., Lang, E.W., Puntonet, C.G., Álvarez, I., López, M., and Gómez-Río, M. (2008). Automatic computer aided diagnosis tool using component-based SVM. In Nuclear Science Symposium Conference Record, 2008. NSS'08. IEEE, (IEEE), pp. 4392-4395.
6. Morra, J.H., Zhuowen Tu, Apostolova, L.G., Green, A.E., Toga, A.W., and Thompson, P.M. (2010). Comparison of AdaBoost and Support Vector Machines for Detecting Alzheimer's Disease Through Automated Hippocampal Segmentation. IEEE Transactions on Medical Imaging 29, 30-43.
7. López, M., Ramírez, J., Górriz, J.M., Salas-Gonzalez, D., Alvarez, I., Segovia, F., and Puntonet, C.G. (2009). Automatic tool for Alzheimer's disease diagnosis using PCA and Bayesian classification rules. Electronics Letters 45, 389-391.
8. Huang, C., Yan, B., Jiang, H., and Wang, D. (2008). Combining Voxelbased Morphometry with Artifical Neural Network Theory in the Application Research of Diagnosing Alzheimer's Disease. (IEEE), pp. 250-254.
9. Padilla, P., Górriz, J.M., Ramirez, J., Chaves, R., Segovia, F., Alvarez, I., Salas-González, D., López, M., and Puntonet, C.G. (2010). Alzheimer's disease detection in functional images using 2D Gabor wavelet analysis. Electronics Letters 46, 556-558.
10. Zhang, Y., Dong, Z., Wu, L., and Wang, S. (2011). A hybrid method for MRI brain image classification. Expert Systems with Applications 38, 10049-10053.
11. Tripoliti, E.E., Fotiadis, D.I., and Manis, G. (2012). Automated Diagnosis of Diseases Based on Classification: Dynamic Determination of the Number of Trees in Random Forests Algorithm. IEEE Transactions on Information Technology in Biomedicine 16, 615-622.
12. Ramirez, J., Chaves, R., Gorriz, J.M., Lopez, M., Lvarez, I.A., Salas-Gonzalez, D., Segovia, F., and Padilla, P. (2009). Computer aided diagnosis of the Alzheimer's disease combining spect-based feature selection and random forest classifiers. In Nuclear Science Symposium Conference Record (NSS/MIC), 2009 IEEE, (IEEE), pp. 2738-2742.
13. Ramírez, J., Górriz, J.M., Segovia, F., Chaves, R., Salas-Gonzalez, D., López, M., Álvarez, I., and Padilla, P. (2010). Computer aided diagnosis system for the Alzheimer's disease based on partial least squares and random forest SPECT image classification. Neuroscience Letters 472, 99-103.
14. Krashenyi, I., Ramírez, J., Popov, A., Górriz, J.M. (2015). Fuzzy inference system for Alzheimer's disease diagnosis. Current Alzheimer Research [submitted for publication].
15. Villain, N., Desgranges, B., Viader, F., de la Sayette, V., Mezenge, F., Landeau, B., Baron, J.-C., Eustache, F., and Chetelat, G. (2008). Relationships between Hippocampal Atrophy, White Matter Disruption, and Gray Matter Hypometabolism in Alzheimer's Disease. Journal of Neuroscience 28, 6174-6181.
16. Bataineh, K.M., Naji, M., and Saqer, M. (2011). A comparison study between various fuzzy clustering algorithms. Jordan Journal of Mechanical and Industrial Engineering 5, 335-343.
17. Zadeh, L.A. (1965). Fuzzy sets. Information and Control 8, 338-353.
18. Zadeh, L.A. (1968). Fuzzy algorithms. Information and Control 12, 94-102.
19. Ashburner, J., and Friston, K.J. (2000). Voxel-Based Morphometry-The Methods. NeuroImage 11, 805-821.
20. Friston, K.J., Holmes, A.P., Worsley, K.J., Poline, J.-P., Frith, C.D., and Frackowiak, R.S. (1994). Statistical parametric maps in functional imaging: A general linear approach. Human Brain Mapping 2, 189-210.
21. Alemán-Gómez, Y., Melie-García, L., Valdés-Hernandez, P. (2006) IBASPM: Toolbox for automatic parcellation of brain structures. 12th Annual Meeting of the Organization for Human Brain Mapping (CDRom in NeuroImage), Vol. 27, No.1.
22. Rice, J.A. (2007). Mathematical statistics and data analysis (Belmont, CA: Thomson/Brooks/Cole).
23. Arlot, S., and Celisse, A. (2010). A survey of cross-validation procedures for model selection. Statistics Surveys 4, 40-79.