  Purpose Predicting the progression of MCI to Alzheimer's disease is an
important step in reducing the progression of the disease. Therefore, many
methods have been introduced for this task based on deep learning. Among these
approaches, the methods based on ROIs are in a good position in terms of
accuracy and complexity. In these techniques, some specific parts of the brain
are extracted as ROI manually for all of the patients. Extracting ROI manually
is time-consuming and its results depend on human expertness and precision.
Method To overcome these limitations, we propose a novel smart method for
detecting ROIs automatically based on Explainable AI using Grad-Cam and a 3DCNN
model that extracts ROIs per patient. After extracting the ROIs automatically,
Alzheimer's disease is predicted using extracted ROI-based 3D CNN. Results We
implement our method on 176 MCI patients of the famous ADNI dataset and obtain
remarkable results compared to the state-of-the-art methods. The accuracy
acquired using 5-fold cross-validation is 98.6 and the AUC is 1. We also
compare the results of the ROI-based method with the whole brain-based method.
The results show that the performance is impressively increased. Conclusion The
experimental results show that the proposed smart ROI extraction, which
extracts the ROIs automatically, performs well for Alzheimer's disease
prediction. The proposed method can also be used for Alzheimer's disease
classification and diagnosis.
