Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Jun 14;15(12):1516.
doi: 10.3390/diagnostics15121516.

Fuzzy Optimized Attention Network with Multi-Instance Deep Learning (FOAN-MIDL) for Alzheimer's Disease Diagnosis with Structural Magnetic Resonance Imaging (sMRI)

Afnan M Alhassan  1 , Nouf I Altmami  1
Affiliations

Fuzzy Optimized Attention Network with Multi-Instance Deep Learning (FOAN-MIDL) for Alzheimer's Disease Diagnosis with Structural Magnetic Resonance Imaging (sMRI)

Afnan M Alhassan et al. Diagnostics (Basel). .

Abstract

Background/Objectives: Alzheimer's disease (AD) is the leading cause of dementia and is characterized by progressive neurodegeneration, resulting in cognitive impairment and structural brain changes. Although no curative treatment exists, pharmacological therapies like cholinesterase inhibitors and NMDA receptor antagonists may deliver symptomatic relief and modestly delay disease progression. Structural magnetic resonance imaging (sMRI) is a commonly utilized modality for the diagnosis of brain neurological diseases and may indicate abnormalities. However, improving the recognition of discriminative characteristics is the primary difficulty in diagnosis utilizing sMRI. Methods: To tackle this problem, the Fuzzy Optimized Attention Network with Multi-Instance Deep Learning (FOA-MIDL) system is presented for the prodromal phase of mild cognitive impairment (MCI) and the initial detection of AD. Results: An attention technique to estimate the weight of every case is presented: the fuzzy salp swarm algorithm (FSSA). The swarming actions of salps in oceans serve as the inspiration for the FSSA. When moving, the nutrient gradients influence the movement of leading salps during global search exploration, while the followers fully explore their local environment to adjust the classifiers' parameters. To balance the relative contributions of every patch and produce a global distinct weighted image for the entire brain framework, the attention multi-instance learning (MIL) pooling procedure is developed. Attention-aware global classifiers are presented to improve the understanding of the integral characteristics and form judgments for AD-related categorization. The Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Australian Imaging, Biomarker, and Lifestyle Flagship Study on Ageing (AIBL) provided the two datasets (ADNI and AIBL) utilized in this work. Conclusions: Compared to many cutting-edge techniques, the findings demonstrate that the FOA-MIDL system may determine discriminative pathological areas and offer improved classification efficacy in terms of sensitivity (SEN), specificity (SPE), and accuracy.

Keywords: Alzheimer’s disease (AD); attention mechanism; convolutional neural network (CNN); fuzzy salp swarm algorithm (FSSA); multi-instance deep learning (MIDL); structural magnetic resonance imaging (sMRI).

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Fuzzy Optimized Attention Network with MIDL (FOA-MIDL).
Figure 2
Figure 2
SEN comparison of sMRI-based studies.
Figure 3
Figure 3
SPE comparison of sMRI-based studies.
Figure 4
Figure 4
AUC comparison of sMRI-based studies.
Figure 5
Figure 5
ACC comparison of sMRI-based studies.
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Bayram E., Caldwell J.Z., Banks S.J. Current understanding of magnetic resonance imaging biomarkers and memory in Alzheimer’s disease. Alzheimer’s Dement. Transl. Res. Clin. Interevent. 2018;4:395–413. doi: 10.1016/j.trci.2018.04.007. - DOI - PMC - PubMed
    1. Shi J., Zheng X., Li Y., Zhang Q., Ying S. Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks for diagnosis of Alzheimer’s disease. IEEE J. Biomed. Health Inform. 2018;22:173–183. doi: 10.1109/JBHI.2017.2655720. - DOI - PubMed
    1. Leandrou S., SPetroudi PAKyriacou C., Reyes-Aldasoro C., Pattichis C.S. Quantitative MRI brain studies in mild cognitive impairment and Alzheimer’s disease: A methodological review. IEEE Rev. Biomed. Eng. 2018;11:97–111. doi: 10.1109/RBME.2018.2796598. - DOI - PubMed
    1. Vounou M., Janousova E., Wolz R., Stein J.L., Thompson P.M., Rueckert D., Montana G., Alzheimer’s Disease Neuroimaging Initiative Sparse reduced-rank regression detects genetic associations with voxel-wise longitudinal phenotypes in Alzheimer’s disease. Neuroimage. 2012;60:700–716. doi: 10.1016/j.neuroimage.2011.12.029. - DOI - PMC - PubMed
    1. Shao W., Peng Y., Zu C., Wang M., Zhang D., Alzheimer’s Disease Neuroimaging Initiative Hypergraph based multi-task feature selection for multimodal classification of Alzheimer’s disease. Comput. Med. Imaging Graph. 2020;80:101663. doi: 10.1016/j.compmedimag.2019.101663. - DOI - PubMed

LinkOut - more resources