Abstract
The medical science is practising on the determination, treatment and aversion of the different disease. An AD is a type of neural dementia that causes a human body-brain especially memory, cognitive skills, and other parts of the brain. The motivation behind this examination is to propose an efficient algorithm structure using deep learning architectures methods and techniques for the perception of Alzheimer disease. In this study, the deep learning architecture structure is created using data normalization, generalized linear neural network (GLNN), regression techniques (softmax), K-means clustering. The detection of Alzheimer’s is done using the combined dataset of the spinal cord and brain. Compared to the previous workflows these methods are capable of detecting the Alzheimer at the minimal timestamp.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
EARLY DIAGNOSIS OF ALZHEIMER’S DISEASE WITH DEEP LEARNING Siqi Liu1, Sidong Liu1, Student Member, IEEE, Weidong Cai1, Member, IEEE, Sonia Pujol2, Ron Kikinis2, Dagan Feng1, Fellow, IEEE (2014).
Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer’s Disease Siqi Liu∗, Student Member, IEEE, Sidong Liu, Student Member, IEEE, Weidong Cai, Member, IEEE, Hangyu Che, Sonia Pujol, Ron Kikinis, Dagan Feng, Fellow, IEEE, Michael J. Fulham, and ADNI (2014).
A Novel Grading Biomarker for the Prediction of Conversion from Mild Cognitive Impairment to Alzheimer’s Disease Tong Tong, Qinquan Gao∗, Ricardo Guerrero, Christian Ledig, Liang Chen Daniel Rueckert and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (2015).
AN EFFICIENT 3D DEEP CONVOLUTIONAL NETWORK FOR ALZHEIMER’S DISEASE DIAGNOSIS USING MR IMAGES Karl Backstrom, Mahmood Nazari, Irene Yu-Hua Gu, Asgeir Store Jakola (2018).
Optimal unsupervised learning in a single layer linear feedforward neural network Author links open overlay panelterence D.Sanger Massachusetts Institute of Technology USA Received 31 October 1988, Accepted 26 April 1989, Available online 6 March (2003).
G. McKhann et al., “Clinical diagnosis of Alzheimer’s disease report of the NINCDSADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s disease,” Neurology, vol. 34, no. 7, pp. 939–939, 1984.
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, DatasetCharacteristicsandTransferLearning Hoo-Chang Shin, Member, IEEE, Holger R. Roth, Mingchen Gao, Le Lu, Senior Member, IEEE, Ziyue Xu, Isabella Nogues, Jianhua Yao, Daniel Mollura, and RonaldM. Summers (1999).
Edge Detection for diagnosis early Alzheimer’s disease by Using Weibull Distribution Wafaa Kamel Al-Jibory, Ali El-Zaart Department of Mathematics and Computer Science Faculty of Science, Beirut Arab University (2013).
Machine Learning-Based Method for Personalized and Cost-Effective Detection of Alzheimer’s Disease Javier Escudero∗, Member, IEEE, Emmanuel Ifeachor, Member, IEEE, John P. Zajicek, Colin Green, James Shearer, and Stephen Pearson, for the Alzheimer’s Disease Neuroimaging Initiative (2012).
NMF-SVM Based CAD Tool Applied to Functional Brain Images for the Diagnosis of Alzheimer’s Disease P. Padilla∗, M. López, J. M. Górriz, J. Ramírez, D. Salas-González, I. Álvarez, and The Alzheimer’s Disease Neuroimaging Initiative (2011).
A novel gene selection method using GA/SVM and Fisher criteria in Alzheimer’s disease Seyede Zahra Paylakhi1, Sadjaad Ozgoli1,Seyed Hassan Paylakhi2 1- Systems, Life science and Control Engineering Lab, Electrical & Computer Engineering School, Tarbiat Modares University, Tehran, Iran 2- Department of Ophthalmology, University of California, San Francisco, San Francisco, CA 94143, USA (2015).
IDENTIFYING THE CANDIDATE GENES FOR ALZHEIMER’S DISEASE BASED ON THE REJECTION REGION OF T TEST GUI-QIONG ZHU1, PEI-HUI YANG2 1Computer Science, Sichuan Normal University, Chengdu 610101, China 2Sichuan Post Telecommunication College, Chengdu 610067, China (2016).
M. Grundman et al., “Mild cognitive impairment can be distinguished from Alzheimer disease and normal ageing for clinical trials,” Archives of Neurology, vol. 61, no. 1, pp. 59–66, (2004).
K. G. Yiannopoulou and S. G. Papageorgiou, “Current and future treatments for Alzheimer’s disease,” Therapeutic advances in neurological disorders, vol. 6, no. 1, pp. 19–33, (2013).
C. Misra et al., “Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: results from ADNI,” NeuroImage, vol. 44, no. 4, pp. 1415–1422, (2009).
M. Chupin et al., “Fully automatic hippocampus segmentation and classification in Alzheimer’s disease and mild cognitive impairment applied to data from ADNI,” Hippocampus, vol. 19, no. 6, pp. 579– 587, (2009).
J. Ye, T. Wu, J. Li, and K. Chen, “Machine learning approaches for the neuroimaging study of Alzheimer’s disease,” Computer, vol. 44, no. 4, pp. 99–101, Apr. (2011).
C.-L. Chi, W. N. Street, and D. A. Katz, “A decision support system for cost-effective diagnosis,” Artif. Intell. Med., vol. 50, no. 3, pp. 149–161, Nov. (2010).
Q. Zhou, M. Goryawala, M. Cabrerizo, J. Wang, W. Barker, R. Duara, and M. Adjouadi, et al. “An Optimal Decisional Space for the Classification of Alzheimer’s Disease and Mild Cognitive Impairment “ IEEE Trans. on Biomedical Engineering, DOI: https://doi.org/10.1109/TBME.2014.2310709, 8 pages, (2014).
Q. Zhou, M. Goryawala, M. Cabrerizo, W. Barker, R. Duara, and M. Adjouadi, “Significance of normalization on anatomical MRI measures in predicting Alzheimer's disease,” ScientificWorldJournal, vol. 2014, pp. 541802, (2014).
Acknowledgement
I would like to thank my guide for supporting me constantly, for helping me giving thoughts and good help through the time of my study.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Malik, V., Maheswari, S. (2020). Deep Learning Architectures for Medical Diagnosis. In: Smys, S., Iliyasu, A.M., Bestak, R., Shi, F. (eds) New Trends in Computational Vision and Bio-inspired Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-41862-5_161
Download citation
DOI: https://doi.org/10.1007/978-3-030-41862-5_161
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-41861-8
Online ISBN: 978-3-030-41862-5
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)