Early Stage Detection of Alzheimer's Disease With Microsoft Azure Based Deep Learning

The early detection and diagnosis of Alzheimer's disease (AD) represent a pivotal aspect of ensuring effective patient care and timely intervention. This research introduces an innovative approach that harnesses the capabilities of Microsoft Azure-based custom vision technology for AD classification. The study primarily centers around the analysis of magnetic resonance imaging (MRI) scans as the primary input data, categorizing these scans into two distinct categories: Cognitive Normal and Cognitive Impairment. To accomplish this, we employ transfer learning, leveraging a pre-trained Microsoft Azure Custom Vision model fine-tuned specifically for multi-class AD classification. The proposed work shows better results with the best validation average accuracy on the test data of AD. This test accuracy score is significantly higher in comparison with existing works. This proposed solution showcases the immense potential of convolutional neural networks and advanced deep learning techniques in the early detection of Alzheimer's disease, thereby paving the way for significantly improved patient care.