Neuroimaging Analysis for Early Diagnosis of ADNI Progression with EfficientNet

This study focuses on the early detection of Alzheimer's disease progression using data from the ADNI Program. Our study resolved how structural MRI and functional MRI are affected together to identify small changes in neurophysiology and function that are indicative of early Alzheimer's disease -using deep learning and feature extraction approaches. Longitudinal data analysis tracks changes over time, providing insight into disease processes. Furthermore, we explore the synergistic potential of different approaches to combine to improve the early detection of $A D$. This study utilises cutting-edge deep learning techniques on multimodal neuroimaging data from ADNI, aiming to find the early stages of AD progression. This research aims to improve understanding of the progression of Alzheimer's disease, paving the way for effective diagnostic tools and treatments tailored to people at risk of the disease. This study can potentially revolutionise early AD intervention techniques and substantially influence clinical practice by utilising the complimentary information supplied by several neuroimaging modalities.