Abstract
This paper is presented towards the development of an automated diagnosis of Alzheimer’s disease (AD) from Magnetic Resonance Images (MRI) using Fuzzy Neural Network (FNN) algorithm. AD is a chronic degenerative disease of the central nervous system. The diagnosis of AD at an early stage is a major concern due to the growing number of the elderly population affected, as well as the lack of a standard and effective diagnosis procedure available to the healthcare providers. Medial Temporal Lobe (MTL) structure in brain has been reported to be involved earliest and most extensively in the pathology of AD. The aim of this research is to develop computing algorithms that can partially or fully automate the extraction of features from MRI of neuroanatomical structures in MTL regions, which aid in diagnosis of AD. Hippocampus volume reductions and ventricular expansions are observed and play significant role in MTL region of brain to identify AD, various other features are also considered and measured. The extracted feature values may be uncertain and it introduces fuzziness in input given to the Artificial Neural Network (ANN). Input uncertainty distribution is effectively solved by designing FNN. The back-propagation neural network algorithm was applied to the analysis of regional patterns corresponding to AD. A trained network was able to successfully classify MRI scans of normal subjects from Mild Cognitive Impairment (MCI), which could be a valuable early indicator of AD. This automated diagnosis will help the neurologist to find the level of disorders and measure the development stage of atrophy in the brain.
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References
J. Wade, T. Mirsen, V. Hachinski, M. Fisman, C. Lau, and H. Merskey. “The clinical diagnosis of Alzheimer’s disease”, Arch. Neurol., vol. 44, pp. 24–29, 1987.
D.J.A. Callen, S.E Black, F. Gao, C.B. Caldwell and J.P. Szalai, “Beyond the Hippocampus: MRI Volumetry confirms widespread Limbic Atrophy in AD”, Neurology, Vol-57, pp.1669–1674, 2007.
JA Kaye, BS Swihart, D Howieson et. al, “Volume loss of hippocampus and temporal lobe in healthy elderly persons destined to develop dementia”, Neurology 1997, 48, pp.1297–1304.
T. Erkinjuntti, D. H. Lee, F. Gao, R. Steenhuis, M. Eliasziw, R. Fry, H. Merskey and V. C. Hachinski, “Temporal lobe atrophy on magnetic resonance imaging in the diagnosis of early Alzheimer disease”, Archives of Neurology, 50:305–310, 1993.
Klaus Fritzsche & Rudiger Dillmann, “Automated MRI-based quantification of the cerebral atrophy providing diagnostic information on mild cognitive impairment and Alzheimer’s disease”, Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems, CBMS’06.
Włodzisław Duch, “Uncertainty of Data, Fuzzy Membership Functions and Multilayer Perceptrons”, IEEE Transactions of Neural Networks, Vol.16, No.1, Jan 2005.
J.S. Kippenhan and J.H. Nagel, “Diagnosis and modelling of Alzheimer’s disease through neural network analysis of PET studies”, Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society.Vol.12, No.3, 1990.
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© 2009 Springer-Verlag Berlin Heidelberg
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S, M.A., Mukunda Rao, M., Shyam Prabhu, N., Simeon, S.D., Karthikeyan, D., Rashmi, S. (2009). Automated Diagnosis of Early Alzheimer’s disease using Fuzzy Neural Network. In: Vander Sloten, J., Verdonck, P., Nyssen, M., Haueisen, J. (eds) 4th European Conference of the International Federation for Medical and Biological Engineering. IFMBE Proceedings, vol 22. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89208-3_345
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DOI: https://doi.org/10.1007/978-3-540-89208-3_345
Publisher Name: Springer, Berlin, Heidelberg
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