Enhancing Alzheimer's Disease Detection and Classification Through Federated Learning-Optimized Deep Convolutional Neural Networks on MRI Data

Early and accurate detection of Alzheimer's Disease (AD) is vital for effective management and intervention. However, the subtle nuances of AD progression and the limited availability of comprehensive datasets pose significant diagnostic challenges. Our study introduces an innovative Deep Convolutional Neural Network (DCNN) model, optimised using Federated Learning (FL) for AD detection and staging from MRI images. This approach respects data privacy by enabling decentralised, multi-institutional model training, and demonstrates exceptional adaptability and precision. On the Open Access Series of Imaging Studies (OASIS) database, our FL-DCNN model achieved a remarkable 97% accuracy rate, suggesting significant potential for enhancing AD diagnostics and patient care.