Attention Gated-VGG with deep learning-based features for Alzheimer's disease classification.

BACKGROUND

Alzheimer's disease (AD) is considered to be one of the neurodegenerative diseases with possible cognitive deficits related to dementia in human subjects. High priority should be put on efforts aimed at early detection of AD.

RESEARCH DESIGN AND

METHODS

Here, images undergo a pre-processing phase that integrates image resizing and the application of median filters. After that, processed images are subjected to data augmentation procedures. Feature extraction from WOA-based ResNet, together with extracted convolutional neural network (CNN) features from pre-processed images, is used to train proposed DL model to classify AD. The process is executed using the proposed Attention Gated-VGG model.

RESULTS

The proposed method outperformed normal methodologies when tested and achieved an accuracy of 96.7%, sensitivity of 97.8%, and specificity of 96.3%.

CONCLUSION

The results have proven that Attention Gated-VGG model is a very promising technique for classifying AD.