Alzheimer's Disease Detection and Classification in Biomarkers Using Deep Learning

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that poses significant diagnostic challenges, particularly in its early stages. Alzheimer's Disease (AD) diagnosis by integrating multimodal data, including brain MRI scans, genetic biomarkers, and cognitive assessments. Traditional diagnostic methods face challenges such as time consumption, subjectivity, and limited accuracy. Conventional machine learning models also struggle to capture complex patterns within diverse data types. By leveraging advanced deep learning techniques, this model will combine heterogeneous data to improve prediction accuracy and diagnostic performance. The framework will be evaluated against unimodal methods to assess its superiority. Additionally, the project will identify critical biomarkers, genetic factors, and cognitive tests most significant for early AD detection, facilitating earlier intervention and personalized treatment strategies, ultimately improving patient outcomes.