This model detects Alzheimer’s Disease (AD) using the ResNet-18 model on Magnetic Resonance Imaging (MRI). In this model, we propose a method to utilise transfer learning in 3D CNNs, which allows the transfer of knowledge from 2D image datasets (ImageNet) to a 3D image dataset. To build 3D ResNet-18, 2D filters of 2D ResNet-18 were extended in the third dimension to have 3D filters. The remaining layers were adjusted according to the new filters. Then, the entire MRIs were used for training 3D ResNet-18 to make one decision per person.

Our results show that introducing transfer learning to a 3D CNN improves an AD detection system's accuracy. This approach achieved 96.88% accuracy, 100% sensitivity, and 93.75% specificity on our ADNI dataset.

There are currently some sample images in this folder. To have access to more images, you need to send your application to ADNI (http://adni.loni.usc.edu/data-samples/access-data/).

Before applying your MRI data, you should register MRI scans to the MNI space using the SPM12 toolbox.