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.
Ebrahimi, Amir, et al. “Introducing Transfer Learning to 3D ResNet-18 for Alzheimer’s Disease Detection on MRI Images.” 2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ), IEEE, 2020, doi:10.1109/ivcnz51579.2020.9290616.
Ebrahimi, Amir, et al. “Convolutional Neural Networks for Alzheimer’s Disease Detection on MRI Images.” Journal of Medical Imaging, vol. 8, no. 02, SPIE-Intl Soc Optical Eng, Apr. 2021, doi:10.1117/1.jmi.8.2.024503.
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