NeuroScan: Deep Learning based model for Early detection of Alzheimer’s disease for Health Care Sector
DOI:
https://doi.org/10.52783/jns.v14.2343Keywords:
NeuroScan framework, Deep learning algorithms, Brain imaging, Traditional machine learning techniques, Alzheimer’s diseaseAbstract
Alzheimer's disease has become a silent epidemic, supported by serious records and long-term predictions, and is currently the seventh biggest cause of mortality. Memory, behaviour, and language are all severely damaged in those who suffer from this terrible illness. Early detection is essential for expanding treatment options, but it is still a difficult undertaking because there aren't enough effective cures and precise diagnoses. Although classic machine learning methods and deep learning approaches been employed in numerous research investigations, their diagnostic abilities are frequently constrained by underlying limitations. In order to overcome this, we suggest a unique deep learning-based paradigm for Alzheimer's disease early detection. Utilizing deep learning techniques, our NeuroScan framework analyses a variety of data sources, including brain imaging. We also look at relevant research on Alzheimer's illness and consider how deep learning can help with early-stage diagnosis. We enhance the Alzheimer Disease Neuroimaging Initiative (ADNI) dataset to highlight the effectiveness of our method and its exceptional performance. With the aid of a sizable MRI dataset that includes both healthy and diseased people, this research proposes a state-of-the-art, user-friendly, automated deep learning method for predicting Alzheimer's disease.
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Copyright (c) 2025 E. Lalitha, Jaya Prakash Sunkavalli, Chunduru Anilkumar, A Naga Kalyani, Naresh Tangudu
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