Alzheimer`s disease (AD) is a perpetual neurological disorder primarily affecting the brain leading to cognitive deterioration, behavioral problems, and memory loss. Alzheimer’s disease which poses a significant danger to individuals worldwide in accordance with the World Health Organization (WHO). According to a recent study, by the year 2060, 70% of the population would have this condition. With a prevalence rate of 60 to 80 percent among dementia cases globally, Alzheimer`s disease emerges as the leading etiology. Researchers are diligently working on the development of advanced machine learning models to improve the accuracy of skull stripping, specifically for separating neural tissues from non-neural tissue in magnetic resonance imaging (MRI) scans and identifying affected patients. In this paper, we unveil a fresh perspective of modified 3D-UNet architecture for precise brain segmentation and 3D-CNN architecture for classification. We argue for a volumetric analysis of the whole brain instead of localization and context information-based approaches for disease classification. As the dataset possesses the time-series like nature, utilization of the long short-term memory-based LSTM architecture has been utilized for medical analysis using MRI data from multiple regular patients. It enhances disease diagnosis & treatment effectiveness. The proposed approach demonstrates segmentation accuracy of 97% and classification accuracy of 95%. These findings enlighten the potential of LSTM-based analysis for neuro-degenerative diseases like-AD.

Brain Segmentation, 3D U-Net, Disease Classification, 3D CNN, Alzheimer’s disease, MRI, LSTM

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