Deep Learning Approaches for Early Detection of Alzheimer’s Disease Using MRI Scans

The timely diagnosis of Alzheimer’s disease (AD) represents an essential precondition for proper treatment as well as effective condition management. Deep learning through convolutional neural networks (CNNs) has transformed medical image analysis thus producing promising AD diagnosis solutions from MRI data. The research analyzes ResNet together with VGG-CNN architectures in Alzheimer’s disease classification while examining how transfer learning overcomes data scarcity problems in medicine. The research presented in this paper utilizes recent findings to evaluate how multi-scale feature extraction methods alongside hybrid models and transfer learning approaches enhance state-of-the-art architectural performance in improving diagnostic accuracy. Maximizing early Alzheimer’s detection requires both the use of pre-trained models combined with customized CNN frameworks which together create a path towards better patient results.