A survey of Alzheimer’s disease diagnosis using deep learning approaches
Abstract
The dementia with the highest prevalence is Alzheimer’s Disease (AD), which can cause a nervous brain syndrome that impairs daily functioning as well as causes gradual remembrance loss by harming brain cells. This fatal condition is exceptional in its field. Early identification of AD is important due to the disease’s global prevalence and evolving threat. Early detection holds promise because it can help predict the health of many people who may be encountered in the future. Therefore, by evaluating the disease’s effects using Magnetic Resonance Imaging (MRI) scans, we may use Artificial Intelligence (AI) technology to categorize AD patients and determine whether or not they will eventually develop the fatal condition. In the area of deep learning methods and analysis, this paper presents essential knowledge and cutting-edge deep learning techniques. The goals of the paper are to advance the knowledge and implementation of medical image processing methods for AD. The paper aims to advance the body of knowledge and promote the creation of efficient and standardised ways in the field by discussing the pertinent techniques and putting recognised recommendations into practise.
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1. Ebrahimighahnavieh MA, Luo S, Chiong R. Deep learning to detect Alzheimer’s disease from neuroimaging: A systematic literature review. Computer Methods and Programs in Biomedicine 2020; 187: 105242. doi: 10.1016/j.cmpb.2019.105242
2. El-Sappagh S, Ali F, Abuhmed T, et al. Automatic detection of Alzheimer’s disease progression: An efficient information fusion approach with heterogeneous ensemble classifiers. Neurocomputing 2022; 512: 203–224. doi: 10.1016/j.neucom.2022.09.009
3. Tuan TA, Pham TB, Kim JY, Tavares JMRS. Alzheimer’s diagnosis using deep learning in segmenting and classifying 3D brain MR images. The International Journal of Neuroscience 2022; 132(7): 689–698. doi: 10.1080/00207454.2020.1835900
4. Al-Shoukry S, Rassem TH, Makbol NM. Alzheimer’s Diseases Detection by Using Deep Learning Algorithms: A Mini-Review. IEEE Access 2020; 8: 77131–77141.
5. Sethi M, Ahuja S, Rani S, et al. An exploration: Alzheimer’s disease classification based on convolutional neural network. Hindawi BioMed Research International 2022; 2022: 8739960. doi: 10.1155/2022/8739960
6. Jo T, Nho K, Saykin AJ. Deep learning in Alzheimer’s disease: Diagnostic classification and prognostic prediction using Neuroimaging data. Frontiers in Aging Neuroscience 2019; 11:220. doi: 10.3389/fnagi.2019.00220
7. Munteanu D, Bejan C, Munteanu N, et al. Deep-learning-based system for assisting people with Alzheimer’s disease. Electronics 2022; 11(19): 3229. doi: 10.3390/electronics11193229
8. Helaly HA, Badawy M, Haikal AY. Deep learning approach for early detection of alzheimer’s disease. Cognitive Computation 2022; 14: 1711–1727. doi: 10.1007/s12559-021-09946-2
9. Kishore P, Kumari CU, Kumar MNVSS, Pavani T. Detection and analysis of Alzheimer’s disease using various machine learning algorithms. Materials Today: Proceedings 2021; 45: 1502–1508. doi: 10.1016/j.matpr.2020.07.645
10. Razavi F, Tarokh MJ, Alborzi M. An intelligent Alzheimer’s disease diagnosis method using unsupervised feature learning. Journal of Big Data 2019; 6:32. doi: 10.1186/s40537-019-0190-7
11. Islam J, Zhang Y. Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks. Brain Informatics 2018; 5: 2. doi: 10.1186/s40708-018-0080-3
12. Noor MBT, Zenia NZ, Kaiser MS, et al. Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia. Brain Informatics 2020; 7(1): 11. doi: 10.1186/s40708-020-00112-2
13. Hosseini-asl E, Keynton R, El-baz A. Alzheimer’s disease diagnostics by adaptation of 3d convolutional network. In: Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP); 25–28 September 2016; Phoenix, AZ, USA.
14. Suganyadevi S, Seethalakshmi V, Balasamy K. A review on deep learning in medical image analysis. International Journal of Multimedia Information Retrieval 2022; 11: 19–38. doi: 10.1007/s13735-021-00218-1
15. Oliveira FPM, Tavares JMRS. Medical image registration: a review. Computer Methods in Biomechanics and Biomedical Engineering 2014; 17(2): 73–93. doi: 10.1080/10255842.2012.670855
16. Fu Y, Lei Y, Wang T, et al. Deep learning in medical image registration: a review. Physics in Medicine & Biology 2020; 65(20): 20TR01. doi: 10.1088/1361-6560/ab843e
17. Haskins G, Kruger U, Yan P. Deep learning in medical image registration: a survey. Machine Vision and Applications 2020; 31: 8. doi: 10.1007/s00138-020-01060-x
18. De Vos BD, Wolterink JM, Jong PA, et al. ConvNet-based localization of anatomical structures in 3D medical images. IEEE Transactions on Medical Imaging 2017; 36(7): 1470–1481. doi: 10.1109/TMI.2017.2673121
19. Song Y, Cai W, Huang H, et al. Region-based progressive localization of cell nuclei in microscopic images with data adaptive modeling. BMC Bioinformatics 2013; 14: 173. doi: 10.1186/1471-2105-14-173
20. Sharma H, Jain JS, Gupta S, Bansal P. Feature extraction and classification of chest X-ray images using CNN to detect pneumonia. In: Proceedings of the 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence); 29–31 January 2020; Noida, India.
21. Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. In: Proceedings of the IEEE Computer Society conference on computer vision and pattern recognition. pp. 1–9.
22. Sun W, Zheng B, Qian W. Computer aided lung cancer diagnosis with deep learning algorithms. In: Proceedings of the SPIE Medical Imaging 2016: Computer-Aided Diagnosis; 24 March 2016; San Diego, California, United States.
23. Teikari P, Santos M, Poon C, Hynynen K. Deep learning convolutional networks for multiphoton microscopy vasculature segmentation. Available online: https://arxiv.org/abs/1606.02382 (accessed on 8 June 2016).
24. Tran PV. A fully convolutional neural network for cardiac segmentation in short axis MRI. Available online: https://arxiv.org/abs/1604.00494 (accessed on 2 April 2016).
25. Yang H, Sun J, Li H, et al. Deep fusion net for multi-atlas segmentation: Application to cardiac MR images. In: Ourselin S, Joskowicz L, Sabuncu M, et al. (editors). Medical Image Computing and Computer-Assisted Intervention—MICCAI 2016. Lecture Notes in Computer Science. Springer, Cham; 2016. Volume 9901.
26. Xie Y, Xing F, Kong X, Su H, Yang L. Beyond Classification: Structured Regression for Robust Cell Detection Using Convolutional Neural Network. Med Image Comput Comput Assist Interv 2015; 9351: 358–365. doi: 10.1007/978-3-319-24574-4_43
27. Castiglioni I, Salvatore C, Ramírez J, Górriz JM. Machine-learning neuroimaging challenge for automated diagnosis of mild cognitive impairment: Lessons learnt. J Neurosci Methods. 2018; 302: 10-13. doi: 10.1016/j.jneumeth.2017.12.019
28. Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Medical Image Analysis 2017; 42: 60–88. doi: 10.1016/j.media.2017.07.005
29. Russakovsky O, Deng J, Su H, et al. ImageNet large scale visual recognition challenge. International Journal of Computer Vision 2015; 115: 211–252. doi: 10.1007/s11263-015-0816-y
30. Zhao J, Zhang M, Zhou Z, et al. Automatic detection and classification of leukocytes using convolutional neural networks. Medical & Biological Engineering & Computing 2017; 55: 1287–1301. doi: 10.1007/s11517-016-1590-x
31. Zhang Q, Xiao Y, Dai W, et al. Deep learning based classification of breast tumors with shear-wave elastography. Ultrasonics 2016; 72: 150–157. doi: 10.1016/j.ultras.2016.08.004
32. Razzak MI, Naz S, Zaib A. Deep learning for medical image processing: Overview, challenges and the future. In: Dey N, Ashour A, Borra S (editors). Classification in BioApps. Springer, Cham; 2017. Volume 26. pp. 323–350.
33. Cui R, Liu M, Li G. Longitudinal analysis for Alzheimer’s disease diagnosis using RNN. In: Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018); 4–7 April 2018; Washington, DC, USA.
34. Pang S, Yang X. Deep convolutional extreme learning machine and its application in handwritten digit classification. Computational Intelligence and Neuroscience 2016; 2016: 3049632. doi: 10.1155/2016/3049632
35. Zeiler MD, Fergus R. Stochastic pooling for regularization of deep convolutional neural networks. Available online: https://arxiv.org/abs/1301.3557v1 (accessed on 16 January 2013).
36. Zeiler MD, Fergus R. Visualizing and understanding convolutional networks. In: Fleet D, Pajdla T, Schiele B, Tuytelaars T (editors). Computer Vision—ECCV 2014, Proceedings of ECCV 2014; 6–12 September 2014; Zurish, Switzerland. Springer, Cham; Volume 8689, pp. 818–833.
37. Yu L, Yang X, Chen H, et al. Volumetric convnets with mixed residual connections for automated prostate segmentation from 3D MR images. Proceedings of the AAAI Conference on Artificial Intelligence 2017; 31(1). doi: 10.1609/aaai.v31i1.10510
38. Yang D, Zhang S, Yan Z, et al. Automated anatomical landmark detection on distal femur surface using convolutional neural network. In: Proceedings of the 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI); 16–19 April 2015; Brooklyn, NY, USA.
39. Wang S, Yao J, Xu Z, Huang J. Subtype Cell Detection with an Accelerated Deep Convolution Neural Network. In: Ourselin S, Joskowicz L, Sabuncu M, et al. (editors). Medical Image Computing and Computer-Assisted Intervention—MICCAI 2016, Proceedings of the MICCAI 2016; 17–21 October 2016; Springer, Cham; 2016. Volume 9901, pp. 640–648.
40. Mushtaq Z, Su SF, Tran QV. Spectral images based environmental sound classification using CNN with meaningful data augmentation. Applied Acoustics 2021; 172: 107581. doi: 10.1016/j.apacoust.2020.107581
41. Merjulah R, Chandra J. Classification of myocardial is chemia in delayed contrast enhancement using machine learning. Intelligent Data Analysis for Biomedical Applications 2019; 209–235. doi: 10.1016/B978-0-12-815553-0.00011-2
42. Oliveira FPM, Tavares JMRS. Medical image registration: a review. Computer Methods in Biomechanics and Biomedical Engineering 2014; 17(2): 73–93. doi: 10.1080/10255842.2012.670856
43. Wang J, Zhang M. Deep FLASH: an efficient network for learning-based Medical Image Registration. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 5 April 2020. pp. 4444–4452.
44. Parmar H, Nutter B, Long R, et al. Spatiotemporal feature extraction and classifcation of Alzheimer’s disease using deep learning 3D-CNN for fMRI data. Journal of Medical Imaging 2020. 7(5): 056001. doi: 10.1117/1.JMI.7.5.056001
45. Ruiz J, Mahmud M, Modasshir M, et al. 3D DenseNet Ensemble in 4-Way Classification of Alzheimer ’s disease. In: Mahmud M, Vassanelli S, Kaiser MS, et al. (editors). Brain Informatics, Proceedings of the 13th International Conference, BI 2020; 19 September 2020; Padua, Italy. Springer, Cham; 2020. Volume 12241.
46. Haskins G, Kruger U, Yan P. Deep learning in medical image registration: a survey. Machine Vision and Applications 2020; 31: 8. doi: 10.1007/s00138-020-01060-x
47. De Vos BD, Wolterink JM, Jong PA, et al. ConvNet-based localization of anatomical structures in 3D medical images. IEEE Transactions on Medical Imaging 2017; 36(7). doi: 10.1109/TMI.2017.2673121
48. Shen D, Wu G, Suk H. Deep learning in medical image analysis. Annual Review of Biomedical Engineering 2017; 19: 221–248. doi: 10.1146/annurev-bioeng-071516-044442
49. Alansary A, Kamnitsas K, Davidson A, et al. Fast fully sutomatic segmentation of the human placenta from motion corrupted MRI. In: Ourselin S, Joskowicz L, Sabuncu M, et al. (editors). Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016, Proceedings of the 19th International Conference; 17–21 October 2016; Athens, Greece. Springer, Cham; 2016. Volume 9901, pp. 589–597.
50. Kalavathi P, Prasath VBS. Methods on skull stripping of MRI head scan images—a review. Journal of Digital Imaging 2016; 29: 365–379. doi: 10.1007/s10278-015-9847-8
51. Mayer A, Greenspan H. An adaptive mean-shift framework for MRI brain segmentation. IEEE Transactions on Medical Imaging 2009; 28(8). doi: 10.1109/TMI.2009.2013850
52. Kim J, Valdes-Hernandez MC, Royle NA, Park J. Hippocampal shape modeling based on a progressive template surface deformation and its verification. IEEE Transactions on Medical Imaging 2015; 34(6). doi: 10.1109/TMI.2014.2382581
53. Lama RK, Gwak J, Park JS, Lee SW. Diagnosis of Alzheimer’s disease based on structural MRI images using a regularized extreme learning machine and PCA features. Journal of Healthcare Engineering 2017; 2017: 5485080. doi: 10.1155/2017/5485080
54. Sarwinda D, Arymurthy AM. Feature selection using kernel PCA for Alzheimer’s disease detection with 3D MR Images of brain. In: Proceedings of the 2013 International Conference on Advanced Computer Science and Information Systems (ICACSIS); 28–29 September 2013; Sanur Bali, Indonesia.
55. Padilla P, Lopez M, Gorriz JM, et al. NMFSVM based CAD tool applied to functional brain images for the diagnosis of Alzheimer’s disease. IEEE Transactions on Medical Imaging 2012; 31(2): 207–216. doi: 10.1109/TMI.2011.2167628
56. Devi KR, Suganyadevi S, Karthik S, Ilayaraja N. Securing medical big data through blockchain technology. In: Proceedings of the 2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS); 2022. pp. 1602–1607.
57. Zhang J, Gao Y, Gao Y, et al. Detecting anatomical landmarks for fast Alzheimer’s disease diagnosis. IEEE Transactions on Medical Imaging 2016; 35(12): 2524–2533. doi: 10.1109/TMI.2016.2582386
58. Bron EE, Smits M, Niessen WJ, Klein S. Feature selection based on the SVM weight vector for classification of dementia. IEEE Journal of Biomedical and Health Informatics 2015; 19(5): 1617–1626. doi: 10.1109/JBHI.2015.2432832
59. McKhann GM, Knopman DS, Chertkow H, et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dementia 2011; 7(3): 263–269. doi: 10.1016/j.jalz.2011.03.005
60. Suganyadevi S, Seethalakshmi V. CVD-HNet: Classifying pneumonia and COVID-19 in chest X-ray images using deep network. Wireless Personal Communications 2022; 126: 3279–3303. doi: 10.1007/s11277-022-09864-y
61. Thies W, Bleiler L. Alzheimer’s disease facts and figures Alzheimer’s Association. Alzheimer’s & Dementia 2015; 8(2): 131–168. doi: 10.1016/j.jalz.2012.02.001
62. Imbimbo BP, Giardina GAM. γ-secretase inhibitors and modulators for the treatment of Alzheimer’s disease: disappointments and hopes. Current Topics in Medicinal Chemistry 2011; 11(12): 1555–1570. doi: 10.2174/156802611795860942
63. Nalivaeva NN, Fisk LR, Belyaev ND, Turner AJ. Amyloid-degrading enzymes as therapeutic targets in Alzheimer’s disease. Current Alzheimer Research 2008; 5(2): 212–224. doi: 10.2174/156720508783954785
64. Wolfe MS. γ-secretase as a target for Alzheimer’s disease. Advances in Pharmacology 2012; 64: 127–153. doi: 10.1016/B978-0-12-394816-8.00004-0
65. Deane RJ. Is RAGE still a therapeutic target for Alzheimer’s disease? Future Medicinal Chemistry 2012; 4(7): 915–925. doi: 10.4155/fmc.12.51
66. Coric V, Van Dyck CH, Salloway S, et al. Safety and tolerability of the γ-secretase inhibitor avagacestat in a phase 2 study of mild to moderate Alzheimer disease. Archives of Neurology 2012; 69(11): 1430–1440. doi:10.1001/archneurol.2012.2194
67. Baranello RJ, Bharani KL, Padmaraju V, et al. Amyloid-beta protein clearance and degradation (ABCD) pathways and their role in Alzheimer’s disease. Current Alzheimer Research 2015; 12(1): 32–46. doi: 10.2174/1567205012666141218140953
68. Miguel-Álvarez M, Santos-Lozano A, Sanchis-Gomar F, et al. Non-steroidal anti-inflammatory drugs as a treatment for Alzheimer’s disease: a systematic review and meta-analysis of treatment effect. Drugs & Aging 2015; 32(2): 139–147. doi: 10.1007/s40266-015-0239-z.
69. Bates KA, Verdile G, Li QX, et al. Clearance mechanisms of Alzheimer’s amyloid-beta peptide: implications for therapeutic design and diagnostic tests. Molecular Psychiatry 2009; 14(5): 469–486. doi: 10.1038/mp.2008.96
70. Spires-Jones TL, Hyman BT. The intersection of amyloid beta and tau at synapses in Alzheimer’s disease. Neuron 2014; 82(4): 756–771. doi: 10.1016/j.neuron.2014.05.004
71. Ferreira ST, Clarke JR, Bomfim TR, De Felice FG. Inflammation, defective insulin signaling, and neuronal dysfunction in Alzheimer’s disease. Alzheimer’s and Dementia 2014; 10(1): S76–S83. doi: 10.1016/j.jalz.2013.12.010
72. De Felice FG, Ferreira ST. Inflammation, defective insulin signaling, and mitochondrial dysfunction as common molecular denominators connecting type 2 diabetes to Alzheimer Disease. Diabetes 2014; 63(7): 2262–2272. doi: 10.2337/db13-1954
73. De Felice FG, Lourenco MV. Brain metabolic stress and neuroinflammation at the basis of cognitive impairment in Alzheimer’s disease. Frontiers in Aging Neuroscience 2015; 7: 94. doi: 10.3389/fnagi.2015.00094.
74. Khagi B, Lee B, Pyun JY, Kwon GR. CNN models performance analysis on MRI images of OASIS dataset for the distinction between healthy and Alzheimer’s patient. In: Proceedings of the 2019 International Conference on Electronics, Information, and Communication (ICEIC); 22–25 January 2019; Auckland, New Zealand.
75. Gomez-Nicola D, Boche D. Post-mortem analysis of neuroinflammatory changes in human Alzheimer’s disease. Alzheimer’s Research & Therapy 2015; 7(1): 42. doi: 10.1186/s13195-015-0126-1.
76. Payan A, Montana G. Predicting Alzheimer’s disease: a neuroimaging study with 3D convolutional neural networks. Available online: https://arxiv.org/abs/1502.02506 (accessed on 9 February 2015).
77. Menting KW, Claassen JAHR. β-secretase inhibitor; a promising novel therapeutic drug in Alzheimer’s Disease. Frontiers in Aging Neuroscience 2014; 6: 165. doi: 10.3389/fnagi.2014.00165.
78. Sarraf S, Tofghi G. Classification of Alzheimer’s disease structural MRI data by deep learning convolutional neural networks. Available online: https://doi.org/10.48550/arXiv.1607.06583 (accessed on 22 July 2016).
79. Korolev S, Safullin A, Belyaev M, Dodonova Y. Residual and plain convolutional neural networks for 3d brain MRI classification. In: Proceedings of the 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017); 18–21 April 2017; Melbourne, VIC, Australia.
80. Khagi B, Lee B, Pyun JY, Kwon GR. CNN models performance analysis on MRI images of OASIS dataset for the distinction between healthy and Alzheimer’s patient. In: Proceedings of the 2019 International Conference on Electronics, Information, and Communication (ICEIC); 22–25 January 2019; Auckland, New Zealand.
81. Wang Y, Yang Y, Guo X, et al. A novel multimodal MRI analysis for Alzheimer’s disease based on convolutional neural network. In: Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 18–21 July 2018; Honolulu, HI, USA.
82. Liu L, Zhao S, Chen H, Wang A. A new machine learning method for identifying Alzheimer’s disease. Simulation Modelling Practice and Theory 2020; 99: 102023. doi: 10.1016/j.simpat.2019.102023
83. Impedovo D, Pirlo G, Vessio G, Angelillo MT. A handwriting-based protocol for assessing Neurodegenerative Dementia. Cognitive Computation 2019; 11: 576–586. doi: 10.1007/s12559-019-09642-2
84. Yan R, Vassar R. Targeting the β secretase BACE1 for Alzheimer’s disease therapy. The Lancet Neurology 2014; 13(3): 319–329. doi: 10.1016/S1474-4422(13)70276-X
85. Manzak D, Cetinel G, Manzak A. Automated classification of Alzheimer’s disease using deep neural network (DNN) by random forest feature elimination. In: Proceedings of the 14th International Conference on Computer Science & Education (ICCSE 2019); 19–21 August 2019.
86. Amin-Naji M, Mahdavinataj H, Aghagolzadeh A. Alzheimer’s disease diagnosis from structural MRI using Siamese convolutional neural network. In: Proceedings of the 4th International Conference on Pattern Recognition and Image Analysis (IPRIA); 6–7 March 2019.
87. Minhas S, Khanum A, Riaz F, et al. A nonparametric approach for mild cognitive impairment to AD conversion prediction: Results on longitudinal data. IEEE Journal of Biomedical & Health Informatics 2017; 21(5): 1403–1410.
88. Taeho J, Kwangsik N, Saykin AJ. Deep learning in Alzheimer’s disease: Diagnostic classification and prognostic prediction using neuroimaging data. Frontiers Aging Neuroscience 2019; 11. doi: 10.3389/fnagi.2019.00220
89. He G, Ping A, Wang X, Zhu Y. Alzheimer’s disease diagnosis model based on three-dimensional full convolutional denseNet. In: Proceedings of the 10th International Conference on Information Technology in Medicine and Education (ITME); 2019.
90. Dubois B, Padovani A, Scheltens P, et al. Timely diagnosis for Alzheimer’s disease: a literature review on benefits and challenges. Journal of Alzheimer’s disease 2016; 49(3): 617–631. doi: 10.3233/JAD-150692
91. Armaanzas R, Iglesias M, Morales DA, Alonso-Nanclares L. Voxel-based diagnosis of Alzheimer’s disease using classifier ensembles. IEEE journal of biomedical and health informatics 2016; 21(3): 778–784. doi: 10.1109/JBHI.2016.2538559
92. Zheng X, Shi J, Zhang Q, et al. Improving MRI-based diagnosis of Alzheimer’s disease via an ensemble privileged information learning algorithm. In: Proceedings of the 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017); 18 April 2017. pp. 456–459.
93. Lahmiri S, Boukadoum M. New approach for automatic classification of Alzheimer’s disease mild cognitive impairment and healthy brain magnetic resonance images. Healthcare Technology Letters 2014; 1(1): 32–36. doi: 10.1049/htl.2013.0022
94. Mesrob L, Magnin B, Colliot O, et al. Identification of atrophy patterns in Alzheimer’s disease based on SVM feature selection and anatomical parcellation. Medical Imaging and Augmented Reality, Proceedings of the 4th International Workshop; 8 August 2008; Tokyo, Japan. Springer; 2009. Volume 5128, pp. 124–132.
95. Fung G, Stoeckel J. SVM feature selection for classification of SPECT images of Alzheimer’s disease using spatial information. Knowledge and Information Systems 2007; 11(2): 243–258. doi: 10.1007/s10115-006-0043-5
96. Coppede F, Grossi E, Buscema M, Migliore L. Application of artificial neural networks to investigate One-Carbon metabolism in Alzheimer’s disease and healthy matched individuals. PLoS One 2013; 8(8): e74012. doi: 10.1371/journal.pone.0074012
97. Shamia D, Balasamy K, Suganyadevi S. A Secure Framework for Medical Image by Integrating Watermarking and Encryption through Fuzzy Based ROI Selection. 2023; 7449–7457.
98. Wang M, Li A, Sekiya M, et al. Transformative network modeling of multi-omics data reveals detailed circuits, key regulators, and potential therapeutics for Alzheimer’s disease. Neuron 2021; 109(2): 257–272.e14. doi: 10.1016/j.neuron.2020.11.002
99. Beckmann ND, Lin WJ, Wang M, et al. Multiscale causal networks identify VGF as a key regulator of Alzheimer’s disease. Nature Communications 2020; 11(1): 3942. doi: 10.1038/s41467-020-17405-z
100. Prince M, Albanese E, Guerchet M, et al. World Alzheimer Report 2014: dementia and risk reduction an analysis of protective and modifiable factors. Available online: https://www.mhinnovation.net/resources/world-alzheimer-report-2014-dementia-and-risk-reduction-analysis-protective-and-modifiable (accessed on 13 July 2023).
101. Ullah HMT, Onik Z, Islam R. Alzheimer’s disease and dementia detection from 3D brain MRI data using deep convolutional. In: Proceedings of the 3rd International Conference for Convergence in Technology (I2CT); April 2018.
102. He G, Ping A, Zhu Y. Alzheimer’s disease diagnosis model based on three-dimensional full convolutional DenseNet. In: Proceedings of the 10th international conference on information technology in medicine and education (ITME). pp. 13–17.
103. Suganyadevi S, Priya SS, Kiruba B, et al. Classification of EEG signals using Machine learning algorithms. In: Proceedings of the 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon); Mysuru, India. 2022, pp. 1–6. doi: 10.1109/MysuruCon55714.2022.9972364.
104. Fuse H, Oishi K, Maikusa N, et al. Detection of Alzheimer’s disease with shape analysis of MRI images. In: Proceedings of the Joint 10th international conference on soft computing and intelligent systems (SCIS) and 19th international symposium on advanced intelligent systems (ISIS); 1 December 2018. pp. 1031–1034.
105. Shahbaz M, Ali S, Guergachi A, et al. Classification of Alzheimer’s disease using machine learning techniques. In: International conference on data science, technology and applications (DATA 2019); pp. 296–303.
106. Albright J. Forecasting the progression of Alzheimer’s disease using neural networks and a novel preprocessing algorithm. Alzheimer’s & Dementia: Translational Research & Clinical Interventions 2019; 5: 483–491. doi: 10.1016/j.trci.2019.07.001
107. Raza M, Awais M, Ellahi W, et al. Diagnosis and monitoring of Alzheimer’s patients using classical and deep learning techniques. Expert Systems with Applications 2019; 136: 353–364. doi: 10.1016/j.eswa.2019.06.038
108. Zhang Y, Wang S, Dong Z. Classification of Alzheimer disease based on structural magnetic resonance imaging by Kernel support vector machine decision tree. Progress In Electromagnetism Research 2014; 144: 171–184. doi: 10.2528/PIER13121310
109. Vidushi AR, Shrivastava AK. Diagnosis of Alzheimer disease using machine learning approaches. International Journal of Advanced Science and Technology 2020; 2(4): 7062–7073.
DOI: https://doi.org/10.32629/jai.v7i3.660
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