ArticlePhysical and engineering sciences in medicine2020
Multi-class diagnosis of Alzheimer's disease using cascaded three dimensional-convolutional neural network.
Article in Physical and engineering sciences in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The future of Alzheimer's disease risk prediction: a systematic review.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Pooled it
- Fractional gradient optimized explainable convolutional neural network for Alzheimer's disease diagnosis.Heliyon · 2024Article
- Classification of cognitive ability of healthy older individuals using resting-state functional connectivity magnetic resonance imaging and an extreme learning machine.BMC medical imaging · 2024Article
- Advanced interpretable diagnosis of Alzheimer's disease using SECNN-RF framework with explainable AI.Frontiers in artificial intelligence · 2024Article
- Application of machine learning in dementia diagnosis: A systematic literature review.Heliyon · 2023Article
- AHANet: Adaptive Hybrid Attention Network for Alzheimer's Disease Classification Using Brain Magnetic Resonance Imaging.Bioengineering (Basel, Switzerland) · 2023Article
- Automatic Detection of Alzheimer's Disease using Deep Learning Models and Neuro-Imaging: Current Trends and Future Perspectives.Neuroinformatics · 2023Review
- Identifying Alzheimer's disease and mild cognitive impairment with atlas-based multi-modal metrics.Frontiers in aging neuroscience · 2023Article
- Development of quantitative and continuous measure for severity degree of Alzheimer's disease evaluated from MRI images of 761 human brains.BMC bioinformatics · 2022Article
- Deep Learning-Based Diagnosis of Alzheimer's Disease.Journal of personalized medicine · 2022Review
- Convolutional neural networks for Alzheimer's disease detection on MRI images.Journal of medical imaging (Bellingham, Wash.) · 2021Article
- Towards practical application of deep learning in diagnosis of Alzheimer's disease.Journal of Alzheimer's disease reportsArticle
Corrections and comments
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Authors and funding
4 authors.
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Abstract
Dementia is a social problem in the aging society of advanced countries. Presently, 46.8 million people affected with dementia worldwide, and it may increase to 130 million by 2050. Alzheimer's disease (AD) is the most common form of dementia. The cost of care for AD patients in 2015 was 818 billion US dollars and is expected to increase intensely due to the increasing number of patients due to the aging society. It isn't easy to cure AD, but early detection is crucial. This paper proposes a multi-class classification of AD, mild cognitive impairment (MCI), and normal control (NC) subjects using three dimensional-convolutional neural network with Support Vector Machine classifier. A cross-sectional study on structural MRI data of 465 subjects, including 132 AD patients, 181 MCI, and 152 NC, is performed in this paper. The highly complex and spatial atrophy patterns of the brain related to Alzheimer's Disease and MCI are extracted from structural MRI images using cascaded layers of the three dimensional convolutional neural network. The hectic process of segmentation and further extraction of handcrafted features is eliminated. The complete image is considered for the processing, thus incorporating every region of the brain for the classification. The features extracted using four cascaded layers of three dimensional-convolutional neural network are fed into the Support Vector Machine classifier. The proposed method achieved 97.77% accuracy which outperforms state of the art, and this algorithm is a promising indicator for the diagnosis of AD.
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