ArticleSensors (Basel, Switzerland)2023
Deep Multi-Branch CNN Architecture for Early Alzheimer's Detection from Brain MRIs.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Clinical pathways matter for multimodal deep learning in early Alzheimer's disease detection.Scientific reports · 2026Article
- A Review of Data Engineering in United States Healthcare Infrastructure.Healthcare (Basel, Switzerland) · 2026Review
- Classifying and diagnosing Alzheimer's disease with deep learning using 6735 brain MRI images.Scientific reports · 2025Article
- Hybrid of DSR-GAN and CNN for Alzheimer disease detection based on MRI images.Scientific reports · 2025Article
- Scoping the Landscape of Deep Learning for Alzheimer's Disease Stage Classification: Methods, Challenges, and Opportunities.BME frontiers · 2025Review
- An efficient method for early Alzheimer's disease detection based on MRI images using deep convolutional neural networks.Frontiers in artificial intelligence · 2025Article
- Isotropic Brain MRI Reconstruction from Orthogonal Scans Using 3D Convolutional Neural Network.Sensors (Basel, Switzerland) · 2024Article
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2 authors.
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Abstract
Alzheimer's disease (AD) is a neurodegenerative disease that can cause dementia and result in a severe reduction in brain function, inhibiting simple tasks, especially if no preventative care is taken. Over 1 in 9 Americans suffer from AD-induced dementia, and unpaid care for people with AD-related dementia is valued at USD 271.6 billion. Hence, various approaches have been developed for early AD diagnosis to prevent its further progression. In this paper, we first review other approaches that could be used for the early detection of AD. We then give an overview of our dataset and propose a deep convolutional neural network (CNN) architecture consisting of 7,866,819 parameters. This model comprises three different convolutional branches, each having a different length. Each branch is comprised of different kernel sizes. This model can predict whether a patient is non-demented, mild-demented, or moderately demented with a 99.05% three-class accuracy. In summary, the deep CNN model demonstrated exceptional accuracy in the early diagnosis of AD, offering a significant advancement in the field and the potential to improve patient care.
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