LCADNet: A Novel Light CNN Architecture for EEG-based Alzheimer Disease Detection
Alzheimer’s disease (AD) is a progressive and incurable neurologi- cal disorder with a rising mortality rate, worsened by error-prone, time-intensive, and expensive clinical diagnosis methods. Automatic AD detection methods using hand-crafted Electroencephalogram (EEG) signal features lack accuracy and reliability. A lightweight convolu- tion neural network for AD detection (LCADNet) is investigated to extract disease-specific features while reducing the detection time. The LCADNet uses two convolutional layers for extracting complex EEG features, two fully connected layers for selecting disease-specific fea- tures, and a softmax layer for predicting AD detection probability. A max-pooling layer interlaced between convolutional layers decreases the time-domain redundancy in the EEG signal. The efficiency of the LCADNet and four pre-trained models using transfer learning is com- pared using a publicly available AD detection dataset. The LCADNet shows the lowest computation complexity in terms of both the num- ber of floating point operations and inference time and the highest classification performance across six measures. The generalization of the LCADNet is assessed by cross-testing it with two other publicly available AD detection datasets. It outperforms existing EEG-based AD detection methods with an accuracy of 98.50%. The LCADNet may be a valuable aid for neurologists and its Python implemen- tation can be found at github.com/SandeepSangle12/LCADNet.git.
Item Type | Article |
---|---|
Additional information | © 2024 Australasian College of Physical Scientists and Engineers in Medicine. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1007/s13246-024-01425-w |
Keywords | electroencephalogram, pre-trained models, convolution neural network, alzheimer’s disease, artificial intelligence |
Date Deposited | 15 May 2025 15:35 |
Last Modified | 31 May 2025 00:44 |
-
picture_as_pdf - paper_LCADNet_20240401_final_submission_AI.pdf
-
subject - Submitted Version
-
lock_clock - Restricted to Repository staff only until 11 June 2025
-
copyright - Available under Unspecified