An MRI Scans-Based Alzheimer’s Disease Detection via Convolutional Neural Network and Transfer Learning

Kwok Tai Chui, Brij B. Gupta, Wadee Alhalabi, Fatma Salih Alzahrani

Research output: Contribution to journalArticlepeer-review

53 Citations (Scopus)

Abstract

Alzheimer’s disease (AD) is the most common type (>60%) of dementia and can wreak havoc on the psychological and physiological development of sufferers and their carers, as well as the economic and social development. Attributed to the shortage of medical staff, automatic diagnosis of AD has become more important to relieve the workload of medical staff and increase the accuracy of medical diagnoses. Using the common MRI scans as inputs, an AD detection model has been designed using convolutional neural network (CNN). To enhance the fine-tuning of hyperparameters and, thus, the detection accuracy, transfer learning (TL) is introduced, which brings the domain knowledge from heterogeneous datasets. Generative adversarial network (GAN) is applied to generate additional training data in the minority classes of the benchmark datasets. Performance evaluation and analysis using three benchmark (OASIS-series) datasets revealed the effectiveness of the proposed method, which increases the accuracy of the detection model by 2.85–3.88%, 2.43–2.66%, and 1.8–40.1% in the ablation study of GAN and TL, as well as the comparison with existing works, respectively.

Original languageEnglish
Article number1531
JournalDiagnostics
Volume12
Issue number7
DOIs
Publication statusPublished - Jul 2022

Keywords

  • Alzheimer’s disease
  • MRI scans
  • automatic diagnosis
  • convolutional neural network
  • deep learning
  • dementia
  • generative adversarial network
  • imbalanced dataset
  • transfer learning

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