Deep Learning-Based Diagnosis Algorithm for Alzheimer's Disease

Abstract

Alzheimer's disease (AD), a degenerative condition affecting the central nervous system, has witnessed a notable rise in prevalence along with the increasing aging population. In recent years, the integration of cutting-edge medical imaging technologies with forefront theories in artificial intelligence has dramatically enhanced the efficiency of identifying and diagnosing brain diseases such as AD. This paper presents an innovative two-stage automatic auxiliary diagnosis algorithm for AD, based on an improved 3D DenseNet segmentation model and an improved MobileNetV3 classification model applied to brain MR images. In the segmentation network, the backbone network was simplified, the activation function and loss function were replaced, and the 3D GAM attention mechanism was introduced. In the classification network, firstly, the CA attention mechanism was added to enhance the model's ability to capture positional information of disease features; secondly, dilated convolutions were introduced to extract richer features from the input feature maps; and finally, the fully connected layer of MobileNetV3 was modified and the idea of transfer learning was adopted to improve the model's feature extraction capability. The results of the study showed that the proposed approach achieved classification accuracies of 97.85% for AD/NC, 95.31% for MCI/NC, 93.96% for AD/MCI, and 92.63% for AD/MCI/NC, respectively, which were 3.1, 2.8, 2.6, and 2.8 percentage points higher than before the improvement. Comparative and ablation experiments have validated the proposed classification performance of this method, demonstrating its capability to facilitate an accurate and efficient automated auxiliary diagnosis of AD, offering a deep learning-based solution for it.

Keywords: Alzheimer’s disease; MRI; attention mechanism; automatic auxiliary diagnosis; deep learning.

Figure 1

Brain MR image preprocessing.

Figure 2

DAGAN structure.

Figure 3

The workflow flowchart of 3D GAM.

Figure 4

Improved 3D DenseNet model structure.

Figure 5

Improved MobileNetV3 structure.

Figure 6

AD automatic auxiliary diagnosis algorithm based on improved MobileNetV3.

Figure 7

Local magnification and comparison of segmentation slices.

Figure 8

Comparison of segmentation results on ADNI dataset with different methods.

Figure 9

AD/NC confusion matrices before and after model improvement.

Figure 10

AD/NC ROC curve before and after model improvement.

Figure 11

MCI/NC confusion matrices before and after model improvement.

Figure 12

MCI/NC ROC curve before and after model improvement.

Figure 13

AD/MCI confusion matrices before and after model improvement.

Figure 14

AD/MCI ROC curve before and after model improvement.

Figure 15

AD/MCI/NC confusion matrices before and after model improvement.

Figure 16

AD/MCI/NC ROC curve before and after model improvement.