EARLY DETECTION OF ALZHEIMER’S DISEASE PROGRESSION WITH MULTI-MODAL BRAIN IMAGING AND CLINICAL DATA

Electronics And Communication Enginnering

Early detection of Alzheimer's Disease (AD) progression is critical for timely intervention and management. This study proposes a novel approach employing multi-modal brain imaging data, specifically Magnetic Resonance Imaging (MRI), augmented with clinical data, to detect early signs of AD progression. The methodology relies on Convolutional Neural Networks (CNNs) to extract intricate features from MRI scans, capturing subtle structural changes indicative of AD progression. By training the CNN on a diverse dataset comprising MRI scans from individuals at varying stages of cognitive impairment and incorporating associated clinical data, including cognitive assessments and demographic information, our model gains a comprehensive understanding of AD progression. The integration of multi-modal imaging data and clinical information enhances the model's ability to detect subtle alterations in brain structure associated with AD progression. Through extensive experimentation and validation on independent datasets, our approach demonstrates promising results in early detection of AD progression, outperforming existing methods in terms of accuracy and sensitivity. This framework offers a non-invasive and scalable approach for early detection of AD progression, enabling timely interventions and personalized treatment strategies. By leveraging the complementary information provided by MRI and clinical data, our model provides valuable insights into disease progression, facilitating improved patient care and outcomes in the battle against Alzheimer's Disease. Keywords: Magnetic Resonance Imaging(MRI), CT scan, Convolutional Neural Network (CNN).

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