Stacked CNN-based multichannel attention networks for Alzheimer disease detection

Topic: Amyloid, tau and blood-test diagnosis

Alzheimer's Disease (AD) is a progressive condition of a neurological brain disorder recognized by symptoms such as dementia, memory loss, alterations in behaviour, and impaired reasoning abilities. Recently, many researchers have been working to develop an effective AD recognition system using deep learning (DL) based convolutional neural network (CNN) model aiming to deploy the automatic medical image diagnosis system. The existing system is still facing difficulties in achieving satisfactory performance in terms of accuracy and efficiency because of the lack of feature ineffectiveness. This study proposes a lightweight Stacked Convolutional Neural Network with a Channel Attention Network (SCCAN) for MRI based on AD classification to overcome the challenges. In the procedure, we sequentially integrate 5 CNN modules, which form a stack CNN aiming to generate a hierarchical understanding of features through multi-level extraction, effectively reducing noise and enhancing the weight's efficacy. This feature is then fed into a channel attention module to select the practical features based on the channel dimension, facilitating the selection of influential features. . Consequently, the model exhibits reduced parameters, making it suitable for training on smaller datasets. Addressing the class imbalance in the Kaggle MRI dataset, a balanced distribution of samples among classes is emphasized. Extensive experiments of the proposed model with the ADNI1 Complete 1Yr 1.5T, Kaggle, and OASIS-1 datasets showed 99.58%, 99.22%, and 99.70% accuracy, respectively. The proposed model's high performance surpassed state-of-the-art (SOTA) models and proved its excellence as a significant advancement in AD classification using MRI images.

@article{hassan2025-39962097,
  author  = {Hassan, Najmul and Miah, Abu Saleh Musa and Suzuki, Kota and Okuyama, Yuichi and Shin, Jungpil},
  title   = {{Stacked CNN-based multichannel attention networks for Alzheimer disease detection}},
  journal = {Scientific Reports},
  year    = {2025},
  doi     = {10.1038/s41598-025-85703-x},
  pmid    = {39962097},
  url     = {https://doi.org/10.1038/s41598-025-85703-x}
}

Key findings

• The proposed model's high performance surpassed state-of-the-art (SOTA) models and proved its excellence as a significant advancement in AD classification using MRI images.

Why it matters

This study proposes a lightweight neural network for classifying Alzheimer's disease from MRI images and reports that it surpassed other models. It is a technical classification study and does not test any treatment, cure, or prevention.

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Study details

• Effect size99.58%; 99.22%; 99.70%
• Evidence strengthLow

Read the original

• DOI: 10.1038/s41598-025-85703-x
• PubMed: 39962097
• Original source →
• Free full text: Scientific Reports →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here.

• Global, regional, and national burden of Alzheimer's disease and other dementias, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016 The Lancet. Neurology · 2018-11-26 · usefulness score 79/100 · Prevention and risk
• Classification and basic pathology of Alzheimer disease Acta neuropathologica · 2009-04-21 · usefulness score 58/100 · Brain tissue and pathology
• Alzheimer's disease: pathogenesis, diagnostics, and therapeutics International journal of nanomedicine · 2019-07-19 · usefulness score 53/100 · TREM2 and immune genetics

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• Amyloid PET Quantification Via End-to-End Training of a Deep Learning Nuclear medicine and molecular imaging · 2019-10-14 · usefulness score 36/100 · Biomarkers
• OASIS-3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer Disease medRxiv · 2019-12-15 · usefulness score 16/100 · Genetics

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