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Convolutional Neural Networks-Based Framework for Early Identification of Dementia Using MRI of Brain Asymmetry

Nitsa J Herzog

Department of Computer Science, Birkbeck College, University of London, Malet Street, London, WC1E 7HZ, UK

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and

George D Magoulas

Birkbeck Knowledge Lab, Birkbeck College, University of London, Malet Street, London, WC1E 7HZ, UK

E-mail Address: [email protected]

Corresponding author.

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https://doi.org/10.1142/S0129065722500538Cited by:11 (Source: Crossref)

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).

Keywords:

References

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