International Journal of Emerging Research in Engineering, Science, and Management
Vol. 3, Issue 3, pp. 20-25, July-Sep 2024.
https://doi.org/10.58482/ijeresm.v3i3.4

Early Detection of Alzheimer's Disease with Deep Learning

Sayyed Nagulmeera*

Nagul Shareef Shaik*

G.Minni#

B Bhagya Lakshmi&

*Research Scholar, Dept. of Computer Science & Engineering, Mohan Babu University, Andhra Pradesh, India

#Professor, Dept. of Computer Science & Engineering, Nimra College of Engineering and Technology, Andhra Pradesh, India.

&Asst. Prof., Dept. of Computer Science & Engineering, Nimra College of Engineering and Technology, Andhra Pradesh, India.

Abstract: Alzheimer’s disease (AD) is a progressive neurological disorder characterised by cognitive decline and memory loss. Early diagnosis is essential for effective treatment, although the complexity of the initial symptoms sometimes delays it. This review addresses the development of a deep learning-based model to aid in the early diagnosis of Alzheimer’s disease using neuroimaging data. Using MRI and PET scans from public datasets such as the Alzheimer’s Disease Neuroimaging Initiative, the proposed version makes use of convolutional neural networks (CNNs) to extract feature extraction and types by looking at brain structure in and on models associated with early Alzheimer’s ailment at the version that tries to decide the values ​​Performance is measured using key parameters along with sensitivity, specificity, accuracy, and area under the curve. The purpose is to develop a predictive tool which could help medical doctors diagnose Alzheimer’s disorder in advance and, in all likelihood, enhance the affected person’s effects via timely intervention.

Keywords: Alzheimer’s Disease, Biomarkers, Convolutional Neural Networks, Deep Learning, MRI, Neuroimaging.

References:

  1. Patterson C., “The state of the art of dementia research: New frontiers,” World Alzheimer Report 2018, Sep 21, 2018.
  2. Gray KR, “Machine learning for image-based classification of Alzheimer’s disease,” Doctoral dissertation, Imperial College London, 2012.
  3. Bonci, S. Fiori, H. Higashi, T. Tanaka, and F. Verdini, “An introductory tutorial on Brain-Computer Interfaces and their applications,” Electronics, vol. 10, no. 5, p. 560, Feb. 2021, doi: 10.3390/electronics10050560.
  4. A. Johnson, N. C. Fox, R. A. Sperling, and W. E. Klunk, “Brain imaging in Alzheimer disease,” Cold Spring Harbor Perspectives in Medicine, vol. 2, no. 4, p. a006213, Jan. 2012, doi: 10.1101/cshperspect.a006213.
  5. “Alzheimer’s Disease Neuroimaging Initiative,” ADNI. https://adni.loni.usc.edu/
  6. Rasmussen and H. Langerman, “Alzheimer’s Disease – Why We Need Early Diagnosis?,” Degenerative Neurological and Neuromuscular Disease, vol. Volume 9, pp. 123–130, Dec. 2019, doi: 10.2147/dnnd.s228939.
  7. Balagopalan, J. Novikova, F. Rudzicz, and M. Ghassemi, “The Effect of Heterogeneous Data for Alzheimer’s Disease Detection from Speech,” arXiv (Cornell University), Jan. 2018, doi: 10.48550/arxiv.1811.12254.
  8. Pietrzak, K. Czarnecka, E. Mikiciuk-Olasik, and P. Szymanski, “New Perspectives of Alzheimer Disease diagnosis – the most popular and future methods,” Medicinal Chemistry, vol. 14, no. 1, Jan. 2018, doi: 10.2174/1573406413666171002120847.
  9. Kamińska, K. Smółka, and G. Zwoliński, “Detection of Mental Stress through EEG Signal in Virtual Reality Environment,” Electronics, vol. 10, no. 22, p. 2840, Nov. 2021, doi: 10.3390/electronics10222840.
  10. Hampel et al., “Future avenues for Alzheimer’s disease detection and therapy: liquid biopsy, intracellular signaling modulation, systems pharmacology drug discovery,” Neuropharmacology, vol. 185, p. 108081, Mar. 2021, doi: 10.1016/j.neuropharm.2020.108081.
  11. Koric et al., “Molecular imaging in the diagnosis of Alzheimer’s disease and related disorders,” Revue Neurologique, vol. 172, no. 12, pp. 725–734, Dec. 2016, doi: 10.1016/j.neurol.2016.10.009.
  12. R. Wolz et al., “Multi-Method analysis of MRI images in early diagnostics of Alzheimer’s disease,” PLoS ONE, vol. 6, no. 10, p. e25446, Oct. 2011, doi: 10.1371/journal.pone.0025446
Download PDF