Efficient Approach for Diagnosis and Detection of Alzheimer Diseases Using Deep Learning

Early and precise diagnosis of Alzheimer's disease (AD) is crucial for timely intervention and improved patient outcomes. Using multimodal neuroimaging data from the ADNI dataset, this study investigates the potential of Deep Learning (DL) for detecting Alzheimer's disease (AD). For AD classification, we test a variety of Convolutional Neural Networks (CNNs), with DenseNet achieving the highest accuracy (90%) and lowest validation loss (0.17). The combination of MRI, PET scans, and possibly other modalities to capture AD complexity and improve diagnostic accuracy is the focus of multimodal fusion. We emphasize the need for collaboration between clinicians, scientists, ethicists, and policy makers to ensure responsible implementation because we recognize the importance of ethical considerations regarding data privacy, patient autonomy, and algorithmic fairness for multimodal DL in AD diagnosis. This prepares for the use its true capacity for further developing promotion analysis and treatment completely.