Differentiating Alzheimer's and healthy brain data in older adults (age > 75) has proven challenging due to highly similar patterns of brain atrophy and image intensities, even though numerous statistical methods and machine learning algorithms have been investigated in both clinical and research settings to extract these patterns from neuroimaging data. Medical image analysis is just one field that has benefited from the widespread use of deep learning technologies in recent years. This research paper proposed AD prediction using transfer learning (AD-TL) methods. The MRI dataset has been normalized using the Multi-Layer Perception model (MLP) with the CNN algorithm. The Image enhancement has been utilized with the Contrast Limited Adaptive histogram equalization CLAHE method. Image segmentation has been done with Watershed Image segmentation. The training has been done with the Residual network (ResNet 50) with Alex net. Finally, the classification has been done with the Deep Convolutional neural network (DCNN) algorithm. According to the experimental data, the classification accuracy of the technique provided in this study may reach 99%.

Alzheimer’s disease, CNN, DCNN, MRI, ResNet, Watershed, Prediction