A Novel Scheme for the Early Diagnosis of Alzheimer’s Disease through MRI Analysis

Sandeep C S, N Vijayakumar, Sukesh A Kumar


Alzheimer disease (AD) is the most common dementia type disease after the age of 65. This leads to cognitive disability to the person being affected. The existing methods are not able to definitely diagnose the disease at an earlier stage. Also, if we can diagnose the disease earlier, treatments can be given at a proper time. Accordingly, an innovative technique should be developed with good accuracy, specificity, and sensitivity. In this scenario, the Magnetic Resonance Imaging (MRI) can be utilized. In this research work,  a method has been proposed using Discrete Wavelet Networks (DWNs). This method gives better results in case of MRI images.

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. Escudero, J, Ifeachor, E, Zajicek, JP, Green, C, Shearer, J, Pearson, S & Alzheimer's Disease Neuroimaging Initiative 'Machine learning-based method for personalized and cost-effective detection of Alzheimer's disease' IEEE Transactions on Biomedical Engineering, vol. 60, no. 1, pp. 164-8, 2013.

. Sandeep C. S., Sukesh Kumar A., “The Early Diagnosis of Alzheimer’s Disease Using Advanced Biomedical Engineering Technology”, Neurological Disorders and Imaging Physics, Volume 2: Application to Autism Spectrum Disorders and Alzheimer's, IOP Expanding Physics, 2019.

. P. Padilla, M. Lopez, J. M. Gorriz, J. Ramirez, D. Salas-Gonzalez and I. Alvarez, "NMF-SVM Based CAD Tool Applied to Functional Brain Images for the Diagnosis of Alzheimer's Disease," in IEEE Transactions on Medical Imaging, vol. 31, no. 2, pp. 207-216, Feb. 2012.

. J. H. Morra, Z. Tu, L. G. Apostolova, A. E. Green, A. W. Toga and P. M. Thompson, "Comparison of AdaBoost and Support Vector Machines for Detecting Alzheimer's Disease Through Automated Hippocampal Segmentation," in IEEE Transactions on Medical Imaging, vol. 29, no. 1, pp. 30-43, Jan. 2010.

. M. S. Tahaei, M. Jalili and M. G. Knyazeva, "Synchronizability of EEG-Based Functional Networks in Early Alzheimer's Disease," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 20, no. 5, pp. 636-641, Sept. 2012.

. Convit, A., de Leon, M.J., Tarshish, C., et al., Hippocampal volume losses in minimally impaired elderly. Lancet 345, 266, 1995.

. Sandeep C S, Sukesh Kumar A, A Review on the Early Diagnosis of Alzheimer’s Disease (AD) through Different Tests, Techniques and Databases AMSE JOURNALS –2015-Series: Modelling C; Vol. 76; N° 1; pp 1-22

.S. Duchesne, A. Caroli, C. Geroldi, C. Barillot, G. B. Frisoni and D. L. Collins, "MRI-Based Automated Computer Classification of Probable AD Versus Normal Controls," in IEEE Transactions on Medical Imaging, vol. 27, no. 4, pp. 509-520, April 2008.

.Simpson, Ivor & W. Woolrich, Mark & L. R. Andersson, Jesper & R. Groves, Adrian & Schnabel, Julia. Ensemble Learning Incorporating Uncertain Registration. IEEE transactions on medical imaging. 32. 10.1109/TMI.2012.2236651, 2012.

.Faro, Alberto & Giordano, D & Spampinato, Concetto & Ullo, Simona & Di Stefano, Angela. Basal Ganglia Activity Measurement by Automatic 3-D Striatum Segmentation in SPECT Images. Instrumentation and Measurement, IEEE Transactions on. 3269 - 3280. 10.1109/TIM.2011.2159315, 2012.

.Bobinski, M., de Leon, M.J., Wegiel, J., et al., The histological validation of post mortem magnetic resonance imaging-determined hippocampal volume in Alzheimer’s disease. Neuroscience 95, 721– 725, 2000.

. Sandeep C S, Sukesh Kumar A ,”A Psychometric Assessment Method for the Early Diagnosis of Alzheimer’s disease”, International Journal of Scientific & Engineering Research -IJSER (ISSN 2229-5518), Volume 8 Issue 3 –MARCH 2017

.Q. Zhang and A. Benveniste, “Wavelet networks,” IEEE Trans. Neural Netw., vol. 3, no. 6, pp. 889–898, Nov. 1992.

.H. H. Szu, B. A. Telfer, and S. L. Kadambe, “Neural network adaptive wavelets for signal representation and classification,” Opt. Eng., vol. 31, no. 9, pp. 1907–1916, Sep. 1992.

.H. Zhang, B. Zhang, W. Huang, and Q. Tian, “Gabor wavelet associative memory for face recognition,” IEEE Trans. Neural Netw., vol. 16, no. 1, pp. 275–278, Jan. 2005.

. O. Jemai, M. Zaied, C. B. Amar, and M. A. Alimi, “Pyramidal hybrid approach: Wavelet network with OLS algorithm-based image classification,” Int. J. Wavel. Multir. Inf. Process., vol. 9, no. 1, pp. 111–130, Mar. 2011.

.Sandeep C S, Sukesh Kumar A, Susanth M J,” Cognitive Examination for the Early Diagnosis of Alzheimer’s Disease”, IEEE International Conference on Trends in Electronics and Informatics, SCAD College of Engineering, Tirunelveli, May 2017, ISBN: 978-1-5090-4257-9, DOI: 10.1109/ICOEI.2017.8300876

. Hinton DR, Sadun AA, Blanks JC, Miller CA. Optic-nerve degeneration in Alzheimer’s disease. N Engl J Med. ;315:485–487, 1986.

.Sadun AA, Bassi CJ. Optic nerve damage in Alzheimer’s disease. Ophthalmology. ; 97:9–17. 1990

.CS Sandeep, A. Sukesh Kumar, K. Mahadevan, P. Manoj, “Analysis of Retinal OCT Images for the Early Diagnosis of Alzheimer's Disease”, Springer-Advances in Intelligent Systems and Computing book series (AISC), Vol.749, pp. 509-520, ISSN 2194-5357, 2018

.R. Galvao, V. M. Becerra, and M. F. Calado, “Linear–wavelet networks,” Int. J. Appl. Math. Comput. Sci., vol. 14, no. 2, pp. 221–232, Aug. 2004.

.S. A. Billings and H. L. Wei, “A new class of wavelet networks for nonlinear system identification,” IEEE Trans. Neural Netw., vol. 16, no. 4, pp. 862–874, Jul. 2005.

.J. Gonzalez-Nuevo, F. Argueso, M. Lopez-Caniego, L. Toffolatti, J. L. Sanz, P. Vielva, and D. Herranz, “The mexican hat wavelet family.application to point source detection in CMB maps,” Mon. Not. Roy. Astron. Soc., vol. 369, pp. 1603–1610, 2006.


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