Bahadori, R.; Kiaei, A.A.; Malek Zadeh, H.; Boush, M.; Abadijou, S.; Safaei, D.; Mehdikhani, A.H. Diagnosing Alzheimer's Disease Levels Using Machine Learning and MRI: A Novel Approach. Preprints2023, 2023061184. https://doi.org/10.20944/preprints202306.1184.v1
APA Style
Bahadori, R., Kiaei, A.A., Malek Zadeh, H., Boush, M., Abadijou, S., Safaei, D., & Mehdikhani, A.H. (2023). Diagnosing Alzheimer's Disease Levels Using Machine Learning and MRI: A Novel Approach. Preprints. https://doi.org/10.20944/preprints202306.1184.v1
Chicago/Turabian Style
Bahadori, R., Danial Safaei and Amir Hossein Mehdikhani. 2023 "Diagnosing Alzheimer's Disease Levels Using Machine Learning and MRI: A Novel Approach" Preprints. https://doi.org/10.20944/preprints202306.1184.v1
Abstract
Alzheimer's disease is a neurological illness that worsens gradually. It is known as the most frequent cause of dementia. Moreover, Alzheimer's disease is a general term for memory loss and other cognitive impairments severe enough to disrupt daily life. The disease often grows in middle-aged and elderly people with the gradual loss of cognitive ability. Since the diagnosis of the illness is time consuming, a novel machine learning approach is proposed in this research to diagnose Alzheimer's levels based on Magnetic Resonance Imaging (MRI). More specifically, to provide distinct information for classification we have implemented the proposed method on a native MRI scans dataset containing three MRI channels; namely T1, T2, and, T2 tirm, which are shown in red, blue and green, respectively. The five middle layers of these three types of MRI scans are combined to form five RGB layers. In addition, we use a Convolutional Neural Network to classify each composed layer, and the final disease type is predicted using majority voting. finally, the F1-Score has been used to assess method performance, indicated that 92% of predictions of the proposed model were accurate.
Keywords
Alzheimer's disease; neurological illness; dementia; machine learning; Magnetic Resonance Im-aging
Subject
Public Health and Healthcare, Public Health and Health Services
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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