Convolutional neural networks-based framework for early identification of dementia using MRI of brain asymmetry
Herzog, Nitsa and Magoulas, George (2022) Convolutional neural networks-based framework for early identification of dementia using MRI of brain asymmetry. International Journal of Neural Systems 32 (12), ISSN 1793-6462.
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Abstract
Computer-aided diagnosis of health problems and pathological conditions has become a substantial part of medical, biomedical, and computer science research. This paper focuses on the diagnosis of early and progressive dementia, building on the potential of deep learning (DL) models. The proposed computational framework exploits a magnetic resonance imaging (MRI) brain asymmetry biomarker, which has been associated with early dementia, and employs DL architectures for MRI image classification. Identification of early dementia is accomplished by an eight-layered convolutional neural network (CNN) as well as transfer learning of pretrained CNNs from ImageNet. Different instantiations of the proposed CNN architecture are tested. These are equipped with Softmax, support vector machine (SVM), linear discriminant (LD), or k -nearest neighbor (KNN) classification layers, assembled as a separate classification module, which are attached to the core CNN architecture. The initial imaging data were obtained from the MRI directory of the Alzheimer’s disease neuroimaging initiative 3 (ADNI3) database. The independent testing dataset was created using image preprocessing and segmentation algorithms applied to unseen patients’ imaging data. The proposed approach demonstrates a 90.12% accuracy in distinguishing patients who are cognitively normal subjects from those who have Alzheimer’s disease (AD), and an 86.40% accuracy in detecting early mild cognitive impairment (EMCI).
Metadata
| Item Type: | Article |
|---|---|
| Keyword(s) / Subject(s): | MRI, brain asymmetry, transfer learning, CNN architecture, dementia. |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Research Centres and Institutes: | Birkbeck Knowledge Lab |
| Depositing User: | George Magoulas |
| Date Deposited: | 20 Oct 2022 16:09 |
| Last Modified: | 15 Sep 2023 00:10 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/49499 |
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