Research Progress on Classification and Diagnosis of Alzheimer's Disease MRI Images Using Neural Network Methods

Weizhe Xiao

doi:10.54097/jnmxyg72

Authors

Weizhe Xiao Interdisciplinary Engineering (Science, Technology and Management), Macau University of Science and Technology, Macao, China

DOI:

https://doi.org/10.54097/jnmxyg72

Keywords:

neural networks, Alzheimer's disease, MRI imaging, deep learning, classification algorithms.

Abstract

With the aging of the population increasing, the dangers of Alzheimer’s disease are increasingly aggravated. Meanwhile, an accurate diagnostic method is important for today's society. This review aims to sort the literature about research progress depending on the classification and diagnosis of Alzheimer’s disease using neural network methods. This review has discussed challenges about the data heterogeneity, incomplete modal imaging, and modal interpretability, through analyzing the applications of multimodal imaging data in the diagnosis of Alzheimer’s disease, and summarized the application of data augmentation, feature fusion, and deep learning algorithms for deep neural network models in this area. Research has found that neural network methods offer significant advantages in enhancing the accuracy and reliability of Alzheimer’s disease diagnosis. Therefore, it is necessary to conduct should conduct deeper research on the model’s generalisability in the future, to promote clinical application. And merge together the related knowledge about traditional medicine and neural networks to realize a more accurate diagnosis of Alzheimer’s disease, to relieve the social burden caused by Alzheimer’s disease.

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References

[1] Suk H I, Shen D. Deep learning-based feature representation for AD/MCI classification. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. Springer, Berlin, Heidelberg, 2013: 583-590.

[2] Chen Z. Joint optimization of cross-modal image synthesis and classification for Alzheimer’s disease. Yunnan University, 2024.

[3] Battineni G, Hussain M A, Chintalapudi N, et al. Improving Alzheimer’s Disease Detection by MRI Using Multimodal Machine Learning Algorithms. IEEE Transactions on Medical Imaging, 2021, 40(11): 2103-2115.

[4] Du X, Novara-Laurentiev J, Prasack J M, et al. Enhancing early detection of cognitive decline in elderly a comparative study utilizing large language models in clinical notes. arXiv preprint arXiv:2403.12345, 2024.

[5] El-Geneedy M, Moustafa H E, Khalifa F, et al. An MRI-based deep learning approach for accurate detection of Alzheimer’s disease. Technical Report TR2023-001, Brigham and Women’s Hospital, Harvard Medical School, 2023.

[6] Sathiyamoorthi V, Ilavarasi A K, Murugeswari K, et al. A deep convolutional neural network-based computer-aided diagnosis system for predicting Alzheimer’s disease from MRI images. Measurement, 2021, 171: 108838.

[7] Houria L, Belkhamsa N, Cherfa A, et al. Multi-modalty MRI for Alzheimer’s disease detection using deep learning. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2022: 1043-1053.

[8] Chen Y, Wang L, Ding F, et al. Automatic classification of Alzheimer’s disease integrating soft NMS and improved ResNet50 with deep learning model. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2024: 100782-100792.

[9] Sorour S E, El-Mageed A A A, Albarrrak K M, et al. Classification of Alzheimer’s disease using MRI data based on Deep learning Techniques. In Proceedings of the 2024 International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2024: 101940.

[10] Li Y. Research on classification algorithm for Alzheimer’s disease based on deep learning joint network and multi-modal feature fusion. Qufu Normal University, 2025.

[11] Zang X. Research on auxiliary diagnosis method for Alzheimer’s disease based on structural magnetic resonance imaging and deep learning. Shandong Normal University, 2025.