U-net based analysis of MRI for Alzheimer's disease diagnosis

Alzheimer's disease (AD) is the most common type of dementia that still has no effective treatment. Accurate classification of AD can help in its diagnosis and selection of the most effective treatment options. In the last decade, several studies have proven the effectiveness of deep learning algorithms for AD diagnosis. In this paper, we propose a U-net style model for AD diagnosis using 3D T1-weighted magnetic resonance images (MRI). Combining with deep supervision has been proved to be effective in improving the performance of the model. Our method has been tested on a subset of ADNI dataset and AIBL dataset and achieves a superior average accuracy of 95.71 +/- 1.36% for AD versus NC (normal control), 90.14 +/- 3.66% for EMCI (early mild cognitive impairment) versus LMCI (late mild cognitive impairment), 90.05 +/- 2.63% for AD versus LMCI, and 87.98 +/- 4.54% for NC versus EMCI, respectively. Besides these binary-classification tasks, we also test this model for multi-class classification task (AD vs. NC vs. EMCI vs. LMCI) and it achieves an accuracy of 86.47 +/- 9.60%. Furthermore, 3D-Grad-CAM method is used to visualize the focused areas of the proposed model. We find that the proposed model pays more attention to the characteristics of the ventricles, hippocampus, and some regions of cortex, which have been proven to be affected by AD.

Automated summary

This paper proposes a deep learning method for Alzheimer's disease diagnosis using 3D T1-weighted magnetic resonance images. The combination of deep supervision improves the model's performance significantly, achieving high accuracy rates in classification tasks. The visualization method, 3D-Grad-CAM, highlights the brain regions focused on by the model.