Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/44293
Title: Alcoholism Identification Based on an AlexNet Transfer Learning Model
Authors: Wang, S-H
Xie, S
Chen, X
Guttery, DS
Tang, C
Sun, J
Zhang, Y-D
First Published: 11-Apr-2019
Publisher: Frontiers Media
Citation: Frontiers in Psychiatry, 2019, 10:205.
Abstract: 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.
DOI Link: 10.3389/fpsyt.2019.00205
ISSN: 1664-0640
Links: https://www.frontiersin.org/articles/10.3389/fpsyt.2019.00205/full
http://hdl.handle.net/2381/44293
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © the authors, 2019. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Description: The datasets for this manuscript are not publicly available because we need approval from our affiliations. Requests to access the datasets should be directed to yudongzhang@ieee.org.
Appears in Collections:Published Articles, Dept. of Cancer Studies and Molecular Medicine

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