Alzheimer’s Disease Diagnosis Based on Collaborative Learning Augmented Algorithms

Gao, Le; Hu, Zhongyi; Li, Zuoyong; Lu, Xingjin; Li, Rui; Tong, Yinsheng; Lin, Donghai

doi:10.1007/978-981-19-6203-5_34

Cited by 3 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results show that the performance of the spatial up-down domain correlation network is significantly improved for the network architecture of this experiment, which makes the model more stable and further improves the accuracy. In addition, to prove the effectiveness of spatial contextual association networks, a comparison is made with the results of other papers, including these papers in addition to those mentioned above [31][32][33][34][35][36][37][38][39][40].The comparison results are shown in Table 8.…”

Section: Experimental Results and Comparisonmentioning

confidence: 99%

Research of CNN-SCN for Early Diagnosis of Alzheimer's Disease

Tong

Huang

et al. 2023

Preprint

View full text Add to dashboard Cite

The early and effective diagnosis of Alzheimer's disease(AD) and mild cognitive impairment(MCI) has received increasing attention in recent years, but most of the existing studies do not pay enough attention to the spatial structure information in structural magnetic resonance images(MRI), and do not go deeper to explore the potential connection between spatial slices and slices, thus making the network models less accurate, poor generalization ability. To explore deeper the connection between spatial context slices, this research is designed to develop a new deep learning method to effectively detect or predict AD by digging into the deeper spatial contextual structural information. In this paper, we design a spatial context network based on 3D convolutional neural network to learn the multi-level structural features of brain MRI images for AD classification. The experimental results show that the model has good stability, accuracy and generalization ability.

show abstract

Section: Experimental Results and Comparisonmentioning

confidence: 99%

Research of CNN-SCN for Early Diagnosis of Alzheimer's Disease

Tong

Huang

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In addition, to prove the effectiveness of spatial contextual association networks, a comparison is made with the results of other papers, including these papers in addition to those mentioned above [31][32][33][34][35][36][37][38][39][40].The comparison results are shown in Table 8. From Table 8, it can be seen that the spatial up-down domain association network proposed in this study has some improvement in performance compared with other methods, which proves that the spatial up-down domain association network can further improve the classification accuracy and stability.…”

Section: Experimental Results and Comparisonmentioning

confidence: 99%

Application of CNN-SCN in early diagnosis of Alzheimer's disease

Tong

Huang

et al. 2022

Preprint

View full text Add to dashboard Cite

Background: The early and effective diagnosis of Alzheimer's disease(AD) and mild cognitive impairment(MCI) has received increasing attention in recent years.There are many machine learning and deep learning methods that are widely used in neural image analysis, among which structural magnetic resonance images play an important role in early diagnosis and intervention for patients with Alzheimer's disease and mild cognitive impairment as a data pattern, and many studies have constructed many network models based on structural magnetic resonance images, but most of the existing studies do not pay enough attention to the spatial structure information in structural magnetic resonance images, and do not go deeper to explore the potential connection between spatial slices and slices,thus making the network models less accurate, poor generalization ability and other problems.We believe that the lesion areas are highly correlated with other areas,and there is a certain relationship between spatial context slices.To explore deeper the connection between spatial context slices and solve the above problems,this research is designed to develop a new deep learning method to effectively detect or predict AD by digging into the deeper spatial contextual structural information. Results: In this paper, we design a spatial context network based on 3D convolutional neural network to learn the multi-level structural features of brain MRI images for AD classification.In addition, this paper designs a new deep learning framework to adapt spatial contextual association networks for early diagnosis of Alzheimer's disease. The experimental results show that the model has good stability, accuracy and generalization ability.Our experimental results showed an accuracy of 92.6% in the AD/CN group, 74.9% in the AD/MCI group, and 76.3% in the MCI/CN group, and several experiments showed that the model has excellent generalization ability. Conclusions: From the comparison of a large number of experimental results, it can be seen that the spatial contextual domain association network can effectively mine the potential information of spatial contextual structural features. The network has better results for the cases that need to go for spatial structure contextual association features.

show abstract