Deep Neural Network Classification method to Alzheimer’s Disease Detection

Kajal Kiran Gulhare, S. P. Shukla, L. K. Sharma

Abstract


Early detection of Alzheimer disease (AD) is important for the management of disease. The human brain Magnetic resonance imaging (MRI) data have been used to detection of Alzheimer disease detection. The detection of AD is quite challenging and thus an automated tool to classify AD can be useful. Deep learning can make major advances in solving such problems. In this study, the longitudinal MRI data in non-demented and demented older adults data is utilized and the image processing technique was adopted for the data segmentation and attribute selection. Finally, deep neural network (DNN) classification is implemented for AD detection. The DNN 96.6% correctly identified AD and the minimum error rate obtained from a DNN. It shows the DNN will be useful for the development of improved computer aided diagnosis tool in MRI data.

Full Text:

PDF

References


World Alzheimer Report, “The global impact of dementia: an analysis of prevalence, incidence, cost and trends”, Alzheimer’s Disease International, London, 2015.

W. Yang et al., “Independent component analysis-based classification of Alzheimer's MRI data”, J Alzheimers Dis., 24(4), pp. 775–783, 2011. doi:10.3233/JAD-2011-101371.

S. Tanna., “Alzheimer Disease”, A Public Health Approach to Innovation, WHO , 2004. http://archives.who.int/prioritymeds/report/background/alzheimer.doc [Last Access on 23/05/2017].

P. S. Mathuranath et al., “Incidence of Alzheimer’s disease in India: A 10 years follow-up study”, Neurol India, 60(6), pp. 625–630, 2012. doi:10.4103/0028-3886.105198

B. Duthey“Background paper 6.11: Alzheimer disease and other dementias”, A Public Health Approach to Innovation, Update on 2004 Background Paper, pp 1–74, 2013. http://www.who.int/medicines/areas/priority_medicines/BP6_11Alzheimer.pdf [Last Access on 23/05/2017].

Y. Zhang. et al. “Detection of subjects and brain regions related to Alzheimer’s disease using 3D MRI scans based on eigen brain and machine learning”, Frontiers in computational Neuroscience, 2015, Vol. 6 Article 66, 2015.doi: 10.3389/fncom.2015.00066

S. Matoug, “Predicting Alzheimer’s disease by segmenting and classifying 3D brain MRI images using clustering technique and SVM classifiers”, Thesis, Laurentian University, Canada., 2015

K. K. Gulhare, S. P. Shukla and L. K. Sharma, “Overview on segmentation and classification for the Alzheimer’s disease detection from brain MRI”, IJCTT, Vol. 43, No. 2, pp. 130-132, 2017.

Y. LeCun, Y.Bengio and G. Hinton, “Deep learning”, Nature, 521, pp. 436-444, 2015. doi:10.1038/nature14539

M. M. Najafabadiet al., “Deep learning applications and challenges in big data , Journal of Big Data”, 2:1, 2015.doi10.1186/s40537-014-0007-7

W. Yang et al., 2011, “Independent component analysis-based classification of Alzheimer's MRI data”, J Alzheimers Dis., Vol. 24, No. 4, pp. 775–783, 2011. doi:10.3233/JAD-2011-101371

B. Al-Nammi, N. Gharaibeh and A. A. Kheshman, “Automated Detection of Alzheimer Disease Using Region Growing technique and Artificial Neural Network”, International Journal of Medical, Health, Biomedical, Bioengineering and Pharmaceutical Engineering, Vol. 7, No. 5, pp. 204-208, 2013.

N. Chaudhary, Y. Aggarwal and R. K. Sinha, “Artificial Neural Network based Classification of Neurodegenerative Diseases”, Advances in Biomedical Engineering Research Vol. 1, No. 1, pp. 1-8, 2013.

J. Tang. et al., “Back propagation artificial neural network for community Alzheimer's disease screening in China”, Neural Regen Res., Vol. 8, No. 3, pp. 270–276, 2013.

F. Coppedè, E. Grossi, M. Buscema, and L. Migliore, “Application of Artificial Neural Networks to Investigate One-Carbon Metabolism in Alzheimer’s Disease and Healthy Matched Individuals”, PLoS ONE Vol. 8, No. 8, e74012, 2013. doi:10.1371/journal.pone.0074012.

D. S. Marcus. et al., 2010, “Open Access Series of Imaging Studies (OASIS): Longitudinal MRI Data in Nondemented and Demented Older Adults”, J CognNeurosci., Vol. 22, No. 12, pp. 2677–2684, 2010. doi:10.1162/jocn.2009.21407.

J. Kim, V. D. Calhoun, E. Shim and J. Lee, “Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia”, NeuroImage 124, pp. 127–146, 2016.




DOI: https://doi.org/10.23956/ijarcsse/V7I6/0259

Refbacks

  • There are currently no refbacks.


Bookmark and Share


© International Journals of Advanced Research in Computer Science and Software Engineering (IJARCSSE)| All Rights Reserved | Powered by Advance Academic Publisher.