Diagnosis of Alzheimer's disease based on regional attention with sMRI gray matter slices.

Background: Alzheimer's disease (AD) is the most common symptom of aggressive and irreversible dementia that affects people's ability of daily life. At present, neuroimaging technology plays an important role in the evaluation and early diagnosis of AD. With the widespread application of artificial intelligence in the medical field, deep learning has shown great potential in computer-aided AD diagnosis based on MRI.

New Method: In this study, we proposed a deep learning framework based on sMRI gray matter slice for AD diagnosis. Compared with the previous methods based on deep learning, our method enhanced gray matter feature information more effectively by combination of slice region and attention mechanism, which can improve the accuracy on the AD diagnosis.

Results: To ensure the performance of our proposed method, the experiment was evaluated on T1 weighted structural MRI (sMRI) images with non-leakage splitting from the ADNI database. Our method can achieve 0.90 accuracy in classification of AD/NC and 0.825 accuracy in classification of AD/MCI, which has better diagnostic performance and advantages than other competitive single-modality methods based on sMRI. Furthermore, we indicated the most discriminative brain MRI slice area determined for AD diagnosis.

Comparison With Existing Methods: Our proposed method based on the regional attention with GM slice has a 1%-8% improvement in accuracy compared with several state-of-the-art methods for AD diagnosis.

Conclusions: The results of experiment indicate that our method can focus more effective features in the gray matter of coronal slices and to achieve a more accurate diagnosis of Alzheimer's disease. This study can provide a more remarkably effective approach and more objective evaluation for the diagnosis of AD based on sMRI slice images.