Early Detection Of Alzheimer's Disease using Transfer Learning on MRI Data

Early detection of Alzheimer's Disease (AD) is crucial for effective patient care and timely intervention. Traditional diagnostic approaches often struggle due to the subtle progression of the disease. This study addresses the challenges of Alzheimer's detection by employing advanced predictive techniques. The proposed Modified Xception model leverages MRI datasets to improve early detection accuracy, achieving an accuracy of 0.96, precision of 0.92, and recall of 0.93. The dataset, sourced from Kaggle, includes four progression stages: Non-dementia (ND), Very Mild dementia (VMD), Mild dementia (MD), and Moderate dementia (MoD). Comprehensive preprocessing methods, including skull stripping, intensity normalization, and data augmentation, ensure high-quality input data. This approach advances neuroimaging analysis, offering a scalable and robust solution for enhancing patient outcomes and optimizing disease management strategies.