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Purpose Predicting the progression of MCI to Alzheimer's disease is animportant step in reducing the progression of the disease. Therefore, manymethods have been introduced for this task based on deep learning. Among theseapproaches, the methods based on ROIs are in a good position in terms ofaccuracy and complexity. In these techniques, some specific parts of the brainare extracted as ROI manually for all of the patients. Extracting ROI manuallyis time-consuming and its results depend on human expertness and precision.Method To overcome these limitations, we propose a novel smart method fordetecting ROIs automatically based on Explainable AI using Grad-Cam and a 3DCNNmodel that extracts ROIs per patient. After extracting the ROIs automatically,Alzheimer's disease is predicted using extracted ROI-based 3D CNN. Results Weimplement our method on 176 MCI patients of the famous ADNI dataset and obtainremarkable results compared to the state-of-the-art methods. The accuracyacquired using 5-fold cross-validation is 98.6 and the AUC is 1. We alsocompare the results of the ROI-based method with the whole brain-based method.The results show that the performance is impressively increased. Conclusion Theexperimental results show that the proposed smart ROI extraction, whichextracts the ROIs automatically, performs well for Alzheimer's diseaseprediction. The proposed method can also be used for Alzheimer's diseaseclassification and diagnosis.

Smart ROI Detection for Alzheimer's disease prediction using explainable AI