Volume : 11, Issue : 01, January – 2024

Title:

NEW AUTOMATED MODELS FOR THE EARLY DIAGNOSIS OF ALZHEIMER’S DISEASE USING MRI IMAGES

Authors :

Yahea Alzahrani, Rana AL Rawashdeh

Abstract :

Alzheimer’s disease (AD) is a progressive and irreversible neurodegenerative disorder that is the leading cause of dementia. It is characterized by the accumulation of abnormal protein deposits in the brain, disrupting normal brain cell function. Symptoms develop slowly and worsen over time, including memory loss, difficulty with language and problem-solving, confusion, and changes in mood and personality. Researchers have proposed and implemented a hybrid framework that combines the Gray wolf optimization algorithm (GWO) and multiple discrete wavelets transform (DWTs) algorithms to achieve early detection using a support vector machine (SVM) and convolutional neural network (CNN). This framework involves several essential steps, including data acquisition, preprocessing, and image-to-signal transformation; feature extraction using four discrete wavelet transform systems (demy, semy, bior1, db8); feature selection through a Gray wolf optimization algorithm (GWO), and SVM-based classification and convolutional neural network (CNN). These steps are critical for developing accurate and reliable machine and deep-learning models for Alzheimer’s disease detection. The study’s results demonstrate the effectiveness of the proposed system, achieving an average accuracy of 94.5% using a support vector machine and 95.4% using a convolutional neural network in detecting Alzheimer’s disease. The integration of machine learning and deep learning algorithms such as SVM, CNN, and Gray wolf optimization for feature selection significantly contributes to the model’s accuracy. This research emphasizes the importance of early detection of Alzheimer’s disease and showcases the machine’s potential and deep learning techniques using brain magnetic resonance images (MRI) to accomplish this objective

Cite This Article:

Please cite this article in press Yahea Alzahrani et al., New Automated Models For The Early Diagnosis Of Alzheimer’s Disease Using MRI Images., Indo Am. J. P. Sci, 2024; 11 (01).

Number of Downloads : 10

References:

1. Huckvale ED, Hodgman MW, Greenwood BB, Stucki DO, Ward KM, Ebbert MT, Kauwe JS, Alzheimer’s Disease Neuroimaging Initiative, Alzheimer’s Disease Metabolomics Consortium, Miller JB. Pairwise Correlation Analysis of the Alzheimer’s disease neuroimaging initiative (ADNI) dataset reveals significant feature correlation. Genes. 2021 Oct 21;12(11):1661.
2. Janghel RR, Rathore YK. Deep convolution neural network based system for early diagnosis of Alzheimer’s disease. Irbm. 2021 Aug 1;42(4):258-67.
3. Song M, Jung H, Lee S, Kim D, Ahn M. Diagnostic classification and biomarker identification of Alzheimer’s disease with random forest algorithm. Brain Sciences. 2021 Apr 2;11(4):453.
4. AlSaeed D, Omar SF. Brain MRI analysis for Alzheimer’s disease diagnosis using CNN-based feature extraction and machine learning. Sensors. 2022 Apr 11;22(8):2911.
5. Bloch L, Friedrich CM, Alzheimer’s Disease Neuroimaging Initiative. Data analysis with Shapley values for automatic subject selection in Alzheimer’s disease data sets using interpretable machine learning. Alzheimer’s Research & Therapy. 2021 Dec;13:1-30.
6. Venugopalan J, Tong L, Hassanzadeh HR, Wang MD. Multimodal deep learning models for early detection of Alzheimer’s disease stage. Scientific reports. 2021 Feb 5;11(1):3254.
7. Herzog NJ, Magoulas GD. Brain asymmetry detection and machine learning classification for diagnosis of early dementia. Sensors. 2021 Jan 24;21(3):778.
8. Battineni G, Hossain MA, Chintalapudi N, Traini E, Dhulipalla VR, Ramasamy M, Amenta F. Improved Alzheimer’s disease detection by MRI using multimodal machine learning algorithms. Diagnostics. 2021 Nov 13;11(11):2103.
9. Lu B, Li HX, Chang ZK, Li L, Chen NX, Zhu ZC, Zhou HX, Li XY, Wang YW, Cui SX, Deng ZY. A practical Alzheimer’s disease classifier via brain imaging-based deep learning on 85,721 samples. Journal of Big Data. 2022 Dec;9(1):1-22.
10. Rowley PA, Samsonov AA, Betthauser TJ, Pirasteh A, Johnson SC, Eisenmenger LB. Amyloid and Tau PET imaging of Alzheimer disease and other neurodegenerative conditions. InSeminars in Ultrasound, CT and MRI 2020 Dec 1 (Vol. 41, No. 6, pp. 572-583). WB Saunders.
11. Johnson KA, Fox NC, Sperling RA, Klunk WE. Brain imaging in Alzheimer disease. Cold Spring Harbor perspectives in medicine. 2012 Apr 1;2(4):a006213.
12. Rayment D, Biju M, Zheng R, Kuruvilla T. Neuroimaging in dementia: an update for the general clinician. Progress in Neurology and Psychiatry. 2016 Mar;20(2):16-20.
13. Lin RH, Wang CC, Tung CW. A machine learning classifier for predicting stable MCI patients using gene biomarkers. International Journal of Environmental Research and Public Health. 2022 Apr 15;19(8):4839.
14. Hamdi M, Bourouis S, Rastislav K, Mohmed F. Evaluation of neuro images for the diagnosis of Alzheimer’s disease using deep learning neural network. Frontiers in Public Health. 2022 Feb 7;10:834032.
15. Li TR, Dong QY, Jiang XY, Kang GX, Li X, Xie YY, Jiang JH, Han Y, Alzheimer’s Disease Neuroimaging Initiative. Exploring brain glucose metabolic patterns in cognitively normal adults at risk of Alzheimer’s disease: A cross-validation study with Chinese and ADNI cohorts. NeuroImage: Clinical. 2022 Jan 1;33:102900.
16. Mormino EC, Betensky RA, Hedden T, Schultz AP, Ward A, Huijbers W, Rentz DM, Johnson KA, Sperling RA. Alzheimer’s Disease Neuroimaging Initiative; Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing; Harvard Aging Brain Study. Amyloid and APOE ε4 interact to influence short‐term decline in preclinical Alzheimer disease. Neurology. 2014;82(20):1760-7.
17. Naz S, Ashraf A, Zaib A. Transfer learning using freeze features for Alzheimer neurological disorder detection using ADNI dataset. Multimedia Systems. 2022 Feb;28(1):85-94.
18. Sun H, Wang A, Wang W, Liu C. An improved deep residual network prediction model for the early diagnosis of Alzheimer’s disease. Sensors. 2021 Jun 18;21(12):4182.
19. Zhu Y, Kim M, Zhu X, Kaufer D, Wu G, Alzheimer’s Disease Neuroimaging Initiative. Long range early diagnosis of Alzheimer’s disease using longitudinal MR imaging data. Medical image analysis. 2021 Jan 1;67:101825.
20. Buyrukoğlu S. Early detection of Alzheimer’s disease using data mining: Comparison of ensemble feature selection approaches. Konya Journal of Engineering Sciences. 2021 Feb 3;9(1):50-61.
21. Al-Shoukry S, Rassem TH, Makbol NM. Alzheimer’s diseases detection by using deep learning algorithms: a mini-review. IEEE Access. 2020 Apr 21;8:77131-41.
22. Islam J, Zhang Y. A novel deep learning based multi-class classification method for Alzheimer’s disease detection using brain MRI data. InBrain Informatics: International Conference, BI 2017, Beijing, China, November 16-18, 2017, Proceedings 2017 (pp. 213-222). Springer International Publishing.
23. Tanveer M, Richhariya B, Khan RU, Rashid AH, Khanna P, Prasad M, Lin CT. Machine learning techniques for the diagnosis of Alzheimer’s disease: A review. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM). 2020 Apr 15;16(1s):1-35.
24. Mofrad SA, Lundervold A, Lundervold AS, Alzheimer’s Disease Neuroimaging Initiative. A predictive framework based on brain volume trajectories enabling early detection of Alzheimer’s disease. Computerized Medical Imaging and Graphics. 2021 Jun 1;90:101910.
25. Odusami M, Maskeliūnas R, Damaševičius R, Krilavičius T. Analysis of features of Alzheimer’s disease: Detection of early stage from functional brain changes in magnetic resonance images using a finetuned ResNet18 network. Diagnostics. 2021 Jun 10;11(6):1071.
26. Yagis E, Citi L, Diciotti S, Marzi C, Atnafu SW, De Herrera AG. 3d convolutional neural networks for diagnosis of alzheimer’s disease via structural mri. In2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS) 2020 Jul 28 (pp. 65-70). IEEE.
27. Reuter M, Schmansky NJ, Rosas HD, Fischl B. Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage. 2012 Jul 16;61(4):1402-18.
28. Lella E, Lombardi A, Amoroso N, Diacono D, Maggipinto T, Monaco A, Bellotti R, Tangaro S. Machine learning and dwi brain communicability networks for alzheimer’s disease detection. Applied Sciences. 2020 Jan 31;10(3):934.
29. Kumar SS, Nandhini M. Entropy slicing extraction and transfer learning classification for early diagnosis of Alzheimer diseases with sMRI. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM). 2021 Apr 21;17(2):1-22.
30. Sharma S, Mandal PK. A comprehensive report on machine learning-based early detection of alzheimer’s disease using multi-modal neuroimaging data. ACM Computing Surveys (CSUR). 2022 Mar 14;55(2):1-44.
31. Mohammed BA, Senan EM, Rassem TH, Makbol NM, Alanazi AA, Al-Mekhlafi ZG, Almurayziq TS, Ghaleb FA. Multi-method analysis of medical records and MRI images for early diagnosis of dementia and Alzheimer’s disease based on deep learning and hybrid methods. Electronics. 2021 Nov 20;10(22):2860.
32. Murugan S, Venkatesan C, Sumithra MG, Gao XZ, Elakkiya B, Akila M, Manoharan S. DEMNET: a deep learning model for early diagnosis of Alzheimer diseases and dementia from MR images. Ieee Access. 2021 Jun 18;9:90319-29.
33. Fan Z, Xu F, Qi X, Li C, Yao L. Classification of Alzheimer’s disease based on brain MRI and machine learning. Neural Computing and Applications. 2020 Apr;32:1927-36.
34. Fuse H, Oishi K, Maikusa N, Fukami T, Japanese Alzheimer’s Disease Neuroimaging Initiative. Detection of Alzheimer’s disease with shape analysis of MRI images. In2018 Joint 10th International Conference on Soft Computing and Intelligent Systems (SCIS) and 19th International Symposium on Advanced Intelligent Systems (ISIS) 2018 Dec 5 (pp. 1031-1034). IEEE.
35. Yildirim M, Cinar A. Classification of Alzheimer’s Disease MRI Images with CNN Based Hybrid Method. Ingénierie des Systèmes d Inf.. 2020 Sep;25(4):413-8.
36. Pinaya WH, Scarpazza C, Garcia-Dias R, Vieira S, Baecker L, F da Costa P, Redolfi A, Frisoni GB, Pievani M, Calhoun VD, Sato JR. Using normative modelling to detect disease progression in mild cognitive impairment and Alzheimer’s disease in a cross-sectional multi-cohort study. Scientific reports. 2021 Aug 3;11(1):15746.
37. Shaikh TA, Ali R. Automated atrophy assessment for Alzheimer’s disease diagnosis from brain MRI images. Magnetic resonance imaging. 2019 Oct 1;62:167-73.