A Comprehensive Study on Early Alzheimer’s Disease Detection through Advanced Machine Learning Techniques on MRI Data

Authors

  • Qunwei Lin
  • Chang Che
  • Hao Hu
  • Xinyu Zhao
  • Shulin Li

DOI:

https://doi.org/10.54097/ajst.v8i1.14334

Keywords:

Alzheimer’s Disease, MRI, Machine Learning, Early Detection, Neural Networks.

Abstract

Alzheimer’s Disease (AD) is a neurodegenerative condition affecting predominantly elderly individuals, repre- senting the most common cause of dementia. Early clinical manifestations of AD include selective memory impairment, and while certain symptomatic improvements can be achieved through treatment, there is currently no cure. Magnetic Resonance Imaging (MRI) is utilized for brain imaging to assess suspected AD patients, providing results that include local and global brain atrophy. Some studies suggest that MRI features can predict the rate of AD decline and guide future treatments. However, to reach this stage, clinicians and researchers must employ machine learning techniques for accurate prediction of the progression from mild cognitive impairment to dementia. We propose the development of a reliable model to assist clinicians in achieving this and predicting early-stage Alzheimer’s Disease.

Downloads

Download data is not yet available.

References

Stefan Klo¨ppel, Cynthia M Stonnington, Carlton Chu, Bogdan Draganski, Rachael I Scahill, Jonathan D Rohrer, Nick C Fox, Clifford R Jack Jr, John Ashburner, and Richard SJ Frackowiak. Automatic classification of mr scans in alzheimer’s disease. Brain, 131(3):681–689, 2008.

Claudia Plant, Stefan J Teipel, Annahita Oswald, Christian Bo¨hm, Thomas Meindl, Janaina Mourao-Miranda, Arun W Bokde, Harald Hampel, and Michael Ewers. Automated detection of brain atrophy patterns based on mri for the prediction of alzheimer’s disease. Neuroimage, 50(1):162–174, 2010.

Elaheh Moradi, Antonietta Pepe, Christian Gaser, Heikki Huttunen, Jussi Tohka, Alzheimer’s Disease Neuroimaging Initiative, et al. Machine learning framework for early mri-based alzheimer’s conversion prediction in mci subjects. Neuroimage, 104:398–412, 2015.

Saman Sarraf, Danielle D DeSouza, John Anderson, Ghassem Tofighi, and Alzheimer’s Disease Neuroimaging Initiativ. Deepad: Alzheimer’s disease classification via deep convolutional neural networks using mri and fmri. BioRxiv, page 070441, 2016.

Simon F Eskildsen, Pierrick Coupe´, Daniel Garc´ıa-Lorenzo, Vladimir Fonov, Jens C Pruessner, D Louis Collins, Alzheimer’s Disease Neuroimaging Initiative, et al. Prediction of alzheimer’s disease in subjects with mild cognitive impairment from the adni cohort using patterns of cortical thinning. Neuroimage, 65:511–521, 2013.

Siqi Liu, Sidong Liu, Weidong Cai, Sonia Pujol, Ron Kikinis, and Dagan Feng. Early diagnosis of alzheimer’s disease with deep learning. In 2014 IEEE 11th international symposium on biomedical imaging (ISBI), pages 1015–1018. IEEE, 2014.

Saima Rathore, Mohamad Habes, Muhammad Aksam Iftikhar, Amanda Shacklett, and Christos Davatzikos. A review on neuroimaging-based classification studies and associated feature extraction methods for alzheimer’s disease and its prodromal stages. NeuroImage, 155:530–548, 2017.

Hao Hu, Shulin Li, Jiaxin Huang, Bo Liu, and Change Che. Casting product image data for quality inspection with xception and data augmentation. Journal of Theory and Practice of Engineering Science, 3(10):42–46, 2023.

Suiyao Chen, Nan Kong, Xuxue Sun, Hongdao Meng, and Mingyang Li. Claims data-driven modeling of hospital time- to-readmission risk with latent heterogeneity. Health care management science, 22:156–179, 2019.

Jing Wu, Ran Tao, Pan Zhao, Nicolas F Martin, and Naira Hovakimyan. Optimizing nitrogen management with deep reinforcement learning and crop simulations. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1712–1720, 2022.

Yishi Shen, Bingxiang Wang, Shijun Deng, Lei Zhai, Hong-mei Gu, Adekunle Alabi, Xiaodan Xia, Yongfang Zhao, Xiaole Chang, Shucun Qin, et al. Surf4 regulates expression of proprotein convertase subtilisin/kexin type 9 (pcsk9) but is not required for pcsk9 secretion in cultured human hepatocytes. Biochimica et Biophysica Acta (BBA)-Molecular and Cell Biology of Lipids, 1865(2):158555, 2020.

Chang Che, Bo Liu, Shulin Li, Jiaxin Huang, and Hao Hu. Deep learning for precise robot position prediction in logistics. Journal of Theory and Practice of Engineering Science, 3(10):36–41, 2023.

Tianbo, Song, Hu Weijun, Cai Jiangfeng, Liu Weijia, Yuan Quan, and He Kun. "Bio-inspired Swarm Intelligence: a Flocking Project With Group Object Recognition." In 2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE), pp. 834-837. IEEE, 2023.

Downloads

Published

21-11-2023

Issue

Section

Articles

How to Cite

Lin, Q., Che, C., Hu, H., Zhao, X., & Li, S. (2023). A Comprehensive Study on Early Alzheimer’s Disease Detection through Advanced Machine Learning Techniques on MRI Data. Academic Journal of Science and Technology, 8(1), 281-285. https://doi.org/10.54097/ajst.v8i1.14334