International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Visualization of Progression from MCI to AD in MRI Images

Author(s) Ms. Anusha R, Kavitha E
Country India
Abstract Memory problems are one of the first signs of cognitive impairment. Difficulties with movement and problems with the sense of smell are some factors related to mild cognitive impairment (MCI), most often in older adults with MCI or at a greater risk of developing Alzheimer's disease (AD). Signs and symptoms may vary from person to person, so it is difficult for the researchers to diagnose the early changes in the brain with MCI vs. cognitively normal people who are at greater risk of developing Alzheimer's disease. Imaging techniques are noninvasive; MRI (magnetic resonance imaging) is the first step in the diagnosis of a brain condition and helps to visualize the structure and function of the brain. Machine learning methods along the MRI imaging process have the highest accuracy rate in achieving the classification of brain abnormalities. This research methodology proposes automated feature extraction using an equilibrium optimization algorithm with a deep learning process for the given MRI images. The goal of the proposed algorithm is to recognize and classify brain abnormalities and differentiate between cognitively normal, MCI, and Alzheimer's disease using the deeper belief network (DBN). The experimental evaluation of the tested data on structural brain MRI images uses the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset.
Keywords (MCI)Mild Cognitive Impairment, (AD)Alzheimer’s disease, (MRI) Magnetic resonance imaging, Equilibrium Optimization Algorithm, Deep learning, (DBN)Deep Belief Network, (ADNI)Alzheimer's Disease Neuroimaging Initiative.
Field Engineering
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-12-12
DOI https://doi.org/10.36948/ijfmr.2025.v07i06.63247
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E-ISSN 2582-2160
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