The early diagnosis of AD is important for patient care and disease management. However, early diagnosis of AD is still challenging. In this work, we proposed a 3D attention model based densely connected Convolution Neural Network to learn the multilevel features of MR brain images for AD classification and prediction. The proposed network was constructed with the emphasis on the interior resource utilization and introduced the attention mechanism into the classification of AD for the first time. Our results showed that the proposed model is effective for AD classification.
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