International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Machine Learning and Deep Learning Approaches for detecting Alzheimerâ€™s Disease (AD): A Review

Author : Archana Singh ¹ Rakesh Kumar ²

Date of Publication :19th July 2022

Abstract: Alzheimer's disease (AD) is the most prevalent chronic disease among the elderly, with a high prevalence. In the treatment of AD, early diagnosis of the patient plays an important role because of severe damage to the brain later. Deep learning (DL) and Machine Learning (ML) have gained popularity and success in the field of medical imaging in recent years. It has become the dominant way of assessing medical pictures, and it has also sparked considerable interest in the diagnosis of Alzheimer's disease. The deep and machine models are more precise and efficient for AD detection than ordinary machine learning techniques. This study provides AD-related biomarkers and feature extraction methods, discusses the use of the machine and deep learning approaches in AD detection, and analyses and summarises AD detection methodologies and models. The results suggest that DL and ML technology performs well in detecting AD.

Reference :

1. S. Gao and D. Lima, “A review of the application of deep learning in the detection of Alzheimer’s disease,” Int. J. Cogn. Comput. Eng., vol. 3, no. December 2021, pp. 1–8, 2022, doi: 10.1016/j.ijcce.2021.12.002.
2. Abdallah, Yousif & Alqahtani, Tariq.,"Research in Medical Imaging Using Image Processing Techniques.",2019, doi: 10.5772/intechopen.84360.
3. J. C. Masdeu, J. L. Zubieta, and J. Arbizu, “Neuroimaging as a marker of the onset and progression of Alzheimer’s disease,” J. Neurol. Sci., vol. 236, no. 1–2, pp. 55–64, 2005, doi: 10.1016/j.jns.2005.05.001.
4. R. Dubey, “a Review on Mri Image,”, May,2015,no. pp. 1–6, , doi: 10.13140/RG.2.1.2131.2486.
5. S. Al-Shoukry, T. H. Rassem, and N. M. Makbol, “Alzheimer’s diseases detection by using deep learning algorithms: A mini-review,” IEEE Access, vol. 8, no. May, pp. 77131–77141, 2020, doi: 10.1109/ACCESS.2020.2989396.
6. F. Han, J. Yang, Y. Liu, and H. Zhao, “Research on preprocessing algorithm for PET-CT image registration,” Proc. - 2010 Int. Conf. Optoelectron. Image Process. ICOIP 2010, vol. 2, no. November, pp. 266–269, 2010, doi: 10.1109/ICOIP.2010.333.
7. L. Xu, G. Liang, C. Liao, G. Den Chen, and C. C. Chang, “An efficient classifier for Alzheimer’s disease genes identification,” Molecules, vol. 23, no. 12, 2018, doi: 10.3390/molecules23123140.
8. R. Ben Ammar and Y. Ben Ayed, “Speech Processing for Early Alzheimer Disease Diagnosis: Machine Learning Based Approach,” Proc. IEEE/ACS Int. Conf. Comput. Syst. App AICCSA, vol. 2018-Novem, pp. 1–8, 2019, doi: 10.1109/AICCSA.2018.8612831.
9. A. Kautzky et al., “Prediction of autopsy verified neuropathological change of Alzheimer’s disease using machine learning and MRI,” Front. Aging Neurosci., vol. 10, no. December, pp. 1–11, 2018, doi: 10.3389/fnagi.2018.00406.
10. Y. T. Zhang and S. Q. Liu, “Individual identification using multi-metric of DTI in Alzheimer’s disease and mild cognitive impairment,” Chinese Phys. B, vol. 27, no. 8, 2018, doi: 10.1088/1674-1056/27/8/088702
11. D. Yao, V. D. Calhoun, Z. Fu, Y. Du, and J. Sui, “An ensemble learning system for a 4-way classification of Alzheimer’s disease and mild cognitive impairment,” J. Neurosci. Methods, vol. 302, pp. 75–81, 2018, doi: 10.1016/j.jneumeth.2018.03.008.
12. I. Iqbal, M. Younus, K. Walayat, M. U. Kakar, and J. Ma, “Automated multi-class classification of skin lesions through deep convolutional neural network with dermoscopic images,” Comput. Med. Imaging Graph., vol. 88, no. November 2020, p. 101843, 2021, doi: 10.1016/j.compmedimag.2020.101843.
13. G. Gosztolya, V. Vincze, L. Tóth, M. Pákáski, J. Kálmán, and I. Hoffmann, “Identifying Mild Cognitive Impairment and mild Alzheimer’s disease based on spontaneous speech using ASR and linguistic features,” Comput. Speech Lang., vol. 53, pp. 181–197, 2019, doi: 10.1016/j.csl.2018.07.007.
14. K. Vaithinathan and L. Parthiban, “A Novel Texture Extraction Technique with T1 Weighted MRI for the Classification of Alzheimer’s Disease,” J. Neurosci. Methods, vol. 318, pp. 84–99, 2019, doi: 10.1016/j.jneumeth.2019.01.011.
15. R. Babapour Mofrad et al., “Decision tree supports the interpretation of CSF biomarkers in Alzheimer’s disease,” Alzheimer’s Dement. Diagnosis, Assess. Dis. Monit., vol. 11, pp. 1–9, 2019, doi: 10.1016/j.dadm.2018.10.004.
16. C. S. Eke, E. Jammeh, X. Li, C. Carroll, S. Pearson, and E. Ifeachor, “Early Detection of Alzheimer’s Disease with Blood Plasma Proteins Using Support Vector Machines,” IEEE J. Biomed. Heal. Informatics, vol. 25, no. 1, pp. 218–226, 2021, doi: 10.1109/JBHI.2020.2984355.
17. J. Ren, B. Zhang, D. Wei, and Z. Zhang, “Identification of Methylated Gene Biomarkers in Patients with Alzheimer’s Disease Based on Machine Learning,” Biomed Res. Int., vol. 2020, 2020, doi: 10.1155/2020/8348147.
18. J. Islam and Y. Zhang, “Early diagnosis of alzheimer’s disease: A neuroimaging study with deep learning architectures,” IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work., vol. 2018-June, pp. 1962–1964, 2018, doi: 10.1109/CVPRW.2018.00247
19. T. Wang, J. L. Qiu, R. G. Qiu, and M. Yu, “Early Detection Models for Persons with Probable Alzheimer’s Disease with Deep Learning,” Proc. 2018 2nd IEEE Adv. Inf. Manag. Commun. Electron. Autom. Control Conf. IMCEC 2018, no. Imcec, pp. 2089–2092, 2018, doi: 10.1109/IMCEC.2018.8469380.
20. X. Bi and H. Wang, “Early Alzheimer’s disease diagnosis based on EEG spectral images using deep learning,” Neural Networks, vol. 114, pp. 119–135, 2019, doi: 10.1016/j.neunet.2019.02.005.
21. K. Oh, Y. C. Chung, K. W. Kim, W. S. Kim, and I. S. Oh, “Classification and Visualization of Alzheimer’s Disease using Volumetric Convolutional Neural Network and Transfer Learning,” Sci. Rep., vol. 9, no. 1, pp. 1–16, 2019, doi: 10.1038/s41598-019-54548-6.
22. J. X. Fong, M. I. Shapiai, Y. Y. Tiew, U. Batool, and H. Fauzi, “Bypassing MRI Pre-processing in Alzheimer’s Disease Diagnosis using Deep Learning Detection Network,” Proc. - 2020 16th IEEE Int. Colloq. Signal Process. its Appl. CSPA 2020, no. Cspa, pp. 219–224, 2020, doi: 10.1109/CSPA48992.2020.9068680.
23. X. Bi, S. Li, B. Xiao, Y. Li, G. Wang, and X. Ma, “Computer aided Alzheimer’s disease diagnosis by an unsupervised deep learning technology,” Neurocomputing, vol. 392, no. 2019, pp. 296–304, 2020, doi: 10.1016/j.neucom.2018.11.111.
24. E. N. Marzban, A. M. Eldeib, I. A. Yassine, and Y. M. Kadah, “Alzheimer’s disease diagnosis from diffusion tensor images using convolutional neural networks,” PLoS One, vol. 15, no. 3, pp. 1–16, 2020, doi: 10.1371/journal.pone.0230409.