LCADNet: A Novel Light CNN Architecture for EEG-based Alzheimer Disease Detection

Pramod Kachare, Digambar Puri, Sandeep B. Sangle, Ibrahim Al-Shourbaji, Abdoh Jabbari, Raimund Kirner, Abdalla Alameen, Hazem Migdady, Laith Abualigah

Research output: Contribution to journalArticlepeer-review


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
Original languageEnglish
JournalPhysical and Engineering Sciences in Medicine
Publication statusPublished - 11 Jun 2024


  • Electroencephalogram
  • Pre-trained models
  • Convolution neural network
  • Alzheimer’s disease


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