Deep Learning Based -Stage Classification of Alzheimer’ Disease from MRI

This study proposes a pretrained weight transfer learning technique using popular pretrained convolutional neural networks such as Vgg16 and Efficientnet-B3. We also used a classical CNN architecture with domain-specific information and architectural alterations to evaluate the performance of the suggested pretrained models. The outcomes of the trials show that the transfer learning method performs better in classifying Alzheimer's disease than the suggested CNN, efficientnetb3, and Vgg16 the potential of transfer learning to enhance the early detection and treatment of the condition. The suggested models display results with an overall accuracy of 95.70% for CNN, Vgg16 and Efficientnet-b3.