Alcoholism identification based on an Alexnet transfer learning model

Shui Hua Wang, Shipeng Xie, Xianqing Chen, David S. Guttery, Chaosheng Tang*, Junding Sun, Yu Dong Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

119 Citations (Scopus)


Aim: This paper proposes a novel alcoholism identification approach that can assist radiologists in patient diagnosis. Method: AlexNet was used as the basic transfer learning model. The global learning rate was small, at 10−4, and the iteration epoch number was at 10. The learning rate factor of replaced layers was 10 times larger than that of the transferred layers. We tested five different replacement configurations of transfer learning. Results: The experiment shows that the best performance was achieved by replacing the final fully connected layer. Our method yielded a sensitivity of 97.44%± 1.15%, a specificity of 97.41 ± 1.51%, a precision of 97.34 ± 1.49%, an accuracy of 97.42 ± 0.95%, and an F1 score of 97.37 ± 0.97% on the test set. Conclusion: This method can assist radiologists in their routine alcoholism screening of brain magnetic resonance images.

Original languageEnglish
Article number205
JournalFrontiers in Psychiatry
Issue numberAPR
Publication statusPublished - 2019
Externally publishedYes


  • Alcoholism
  • AlexNet
  • Convolutional neural network
  • Data augmentation
  • Dropout
  • Local response normalization
  • Magnetic resonance imaging
  • Transfer learning


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