Machine Learning for Early Detection of Alzheimer's Disease from Brain MRI
Keywords:
computed tomography or magnetic resonance imaging scanner, machine learning, deep learning and transfer learning modelsAbstract
Recent years have seen an uptick in the use of computed tomography (CT) and magnetic resonance imaging (MRI) scans to create three-dimensional images of the human body for use in medical image processing studies. Immunisations and medical treatment do not work to prevent or treat chronic diseases. Some examples of chronic ailments are asthma, cancer, heart disease, diabetes, and Alzheimer's disease. Alzheimer's disease is a progressive neurodegenerative illness that destroys both memory and personality over time. Without regular checks, diseases like Alzheimer's could not be seen until they've progressed to a fatal level. Millions of individuals throughout the globe are living with Alzheimer's disease, which is a leading cause of death. The first indicator of Alzheimer's disease may be moderate cognitive and/or behavioural impairment, followed by preclinical illness and, ultimately, full-blown Alzheimer's disease. This machine learning model outperforms state-of-the-art medical ailment prediction methods. Most machine learning algorithms for Alzheimer's disease identification are limited to low-dimensional feature spaces because of the sparsity problem. Research in this article examines the feasibility of using several methods such as deep learning, machine learning, and transfer learning approaches to create an early Alzheimer's disease diagnosis.
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M. Shetty, Deekshitha, M. Bhat and M. Devadiga, "Detection of Alzheimer's Disease Using Machine Learning," 2022 International Conference on Artificial Intelligence and Data Engineering (AIDE), Karkala, India, 2022, pp. 117-120, doi: 10.1109/AIDE57180.2022.10060433.
S. Dixit, A. Gaikwad, V. Vyas, M. Shindikar and K. Kamble, "United Neurological study of disorders: Alzheimer's disease, Parkinson's disease detection, Anxiety detection, and Stress detection using various Machine learning Algorithms," 2022 International Conference on Signal and Information Processing (IConSIP), Pune, India, 2022, pp. 1-6, doi: 10.1109/ICoNSIP49665.2022.10007434.
D. Chaihtra and S. Vijaya Shetty, "Alzheimer’s Disease Detection from Brain MRI Data using Deep Learning Techniques," 2021 2nd Global Conference for Advancement in Technology (GCAT), Bangalore, India, 2021, pp. 1-5, doi: 10.1109/GCAT52182.2021.9587756.
S. Aruchamy, V. Mounya and A. Verma, "Alzheimer’s Disease Classification in Brain MRI using Modified kNN Algorithm," 2020 IEEE International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC), Gunupur Odisha, India, 2020, pp. 1-6, doi: 10.1109/iSSSC50941.2020.9358867.
P. Lodha, A. Talele and K. Degaonkar, "Diagnosis of Alzheimer's Disease Using Machine Learning," 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), Pune, India, 2018, pp. 1-4, doi: 10.1109/ICCUBEA.2018.8697386.
J. Islam and Y. Zhang, "Early Diagnosis of Alzheimer's Disease: A Neuroimaging Study with Deep Learning Architectures," 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA, 2018, pp. 1962-19622, doi: 10.1109/CVPRW.2018.00247.
Vijayalakshmi, V., & Sharmila, K. (2023). Secure Data Transactions based on Hash Coded Starvation Blockchain Security using Padded Ring Signature-ECC for Network of Things. International Journal on Recent and Innovation Trends in Computing and Communication, 11(1), 53–61. https://doi.org/10.17762/ijritcc.v11i1.5986
S. Aruchamy, A. Haridasan, A. Verma, P. Bhattacharjee, S. N. Nandy and S. Ram Krishna Vadali, "Alzheimer’s Disease Detection using Machine Learning Techniques in 3D MR Images," 2020 National Conference on Emerging Trends on Sustainable Technology and Engineering Applications (NCETSTEA), Durgapur, India, 2020, pp. 1-4, doi: 10.1109/NCETSTEA48365.2020.9119923.
S Song, H Lu and Z. Pan, "Automated diagnosis of Alzheimer’s disease using Gaussian mixture model based on cortical thickness", 2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI), pp. 880-883, 2012 Oct 18.
Mr. Dharmesh Dhabliya, Mr. Rahul Sharma. (2012). Efficient Cluster Formation Protocol in WSN. International Journal of New Practices in Management and Engineering, 1(03), 08 - 17. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/7
DT Bui and ND. Hoang, "A Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1. 1) for spatial prediction of floods", Geoscientific Model Development, vol. 10, no. 9, pp. 3391, 2017.
A Kiraly, N Szabo, E Toth et al., "Male brain ages faster: the age and gender dependence of subcortical volumes", Brain Imaging Behav. 2016, vol. 10, pp. 901-910.
X Zhu, Z Lei and H Zi, "A sparse embedding and least variance encoding approach to hashing", IEEE Trans Image Process, vol. 23, no. 9, pp. 3737, 2014.
F. Coppede, E. Grossi, M. Buscema and L. Migliore, Application of Artificial Neural Networks to Investigate One-Carbon Metabolism in Alzheimer's disease and Healthy Matched Individuals, 2013.
P Padilla, M Lpez, JM Grriz, J Ramirez, D Salas-Gonzalez and I. Alvarez, "NMF-SVM based CAD tool applied to functional brain images for the diagnosis of Alzheimer’s disease", IEEE Transactions on medical imaging, vol. 31, no. 2, pp. 207-16, Sep 2011.
A. Badnjevic, M. Cifrek, D. Koruga and D. Osmankovic, "Neurofuzzy classification of asthma and chronic obstructive pulmonary disease", BMC Med Inform Decis Mak BMC Medical Informatics and Decision Making, vol. 15, no. 3, 2015.
JC Breitner, "Dementia-epidemiological considerations nomenclature and a tacit consensus definition", J Geriatr Psychiatry Neurol., vol. 19, no. 3, pp. 129-36, 2006.
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