Image Classification Detection Technology in the Diagnosis of Alzheimer's Disease

Zeyu Ma

doi:10.54254/2755-2721/2025.LD26379

Applied and Computational EngineeringOpen access

Image Classification Detection Technology in the Diagnosis of Alzheimer's Disease

Research Article

Open Access

Image Classification Detection Technology in the Diagnosis of Alzheimer's Disease

Zeyu Ma ^1*

¹ Aulin College, Northeast Forestry University, Harbin, Heilongjiang, China

^*Corresponding author: mzzzy2025@outlook.com

Published on 26 August 2025

ACE Vol.179

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-184-6

ISBN (Online): 978-1-80590-129-7

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Abstract

Alzheimer's disease (AD) is a primary brain degenerative disease that occurs in the elderly and pre - elderly. This disease will produce irreversible brain structural and molecular changes, leading to progressive cognitive and behavioral disorders. The disease has the characteristics of strong concealment and difficult early diagnosis, which also leads to a very difficult diagnosis. Therefore, with the rise of deep learning, researchers began to use image classification detection technology to assist in the diagnosis of Alzheimer's disease. This paper summarizes the application of image classification and detection technology in the diagnosis of Alzheimer's disease from three parts: the application of the single convolutional neural network method in diagnosis, the application of the attention mechanism and the convolutional neural network fusion method in diagnosis, and the application of the fusion model method in diagnosis. At the same time, this paper also makes a comparative analysis of the mainstream related databases, including data scale, queue diversity, data mode, research scenarios, and acquisition methods. The research in this paper not only provides the basis for researchers to select models and data, but also promotes the integration of image technology and Alzheimer's disease diagnosis.

Keywords:

Alzheimer's disease, deep learning, computer-aided diagnosis

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References

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References

[2]. Rostagno, A. A. (2022). Pathogenesis of Alzheimer’s disease. International journal of molecular sciences, 24(1), 107.

[3]. Scheltens, P., De Strooper, B., Kivipelto, M., Holstege, H., Chételat, G., Teunissen, C. E., ... & van der Flier, W. M. (2021). Alzheimer's disease. The Lancet, 397(10284), 1577-1590.

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[11]. Wang, J. X., Li, Y., Li, X., & Lu, Z. H. (2022). Alzheimer's disease classification through imaging genetic data with IGnet. Frontiers in Neuroscience, 16, 846638.

Cite this article

Ma,Z. (2025). Image Classification Detection Technology in the Diagnosis of Alzheimer's Disease. Applied and Computational Engineering,179,68-73.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-MLA 2025 Symposium: Intelligent Systems and Automation: AI Models, IoT, and Robotic Algorithms

ISBN: 978-1-80590-184-6(Print) / 978-1-80590-129-7(Online)

Editor: Hisham AbouGrad

Conference website: https://www.confmla.org/london.html

Conference date: 17 November 2025

Series: Applied and Computational Engineering

Volume number: Vol.179

ISSN: 2755-2721(Print) / 2755-273X(Online)

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