Early diagnosis of Alzheimer’s disease using dual GAN model with pyramid attention networks

ABSTRACT

Alzheimer's disease (AD) is a neurological condition that impairs the patient's cognitive function. The article presents a deep learning architecture using the implicit image from MRI to categories MRI scans and detect AD on time. The annotated data is divided into patients with AD and control groups. After GAN creates the synthetic and real images, the dataset is passed through CNN to detect spatial features from the scans. We used 30 slices from the region of the top brain above the eyes for learning. In order to train the CNNs and evaluate the results, the data is divided using the 10-fold cross-validation evaluating technique to validate the model, the accuracy estimates are 99.67%, 98.76%, respectively.

KEYWORDS:

• Alzheimer’s disease
• MRI image data
• dual GAN and pyramid networks
• deep learning

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No potential conflict of interest was reported by the author(s).

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.