Abstract #2689

CNN-based autoencoder and machine learning model for identifying betel-quid chewers using functional MRI features

Hsin-An Shen¹, Ming-Chou Ho^2,3, and Jun-Cheng Weng^1,4,5

¹Department of Medical Imaging and Radiological Sciences, and Bachelor Program in Artificial Intelligence, Chang Gung University, Taoyuan, Taiwan, ²Department of Psychology, Chung Shan Medical University, Taichung, Taiwan, ³Clinical Psychological Room, Chung Shan Medical University Hospital, Taichung, Taiwan, ⁴Medical Imaging Research Center, Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan, ⁵Department of Psychiatry, Chang Gung Memorial Hospital, Chiayi, Taiwan

Previous studies indicated that betel-quid chewing may cause brain functional alternations, but it cannot be distinguished with human eyes. We used resting-state functional magnetic resonance imaging as input features for machine learning to classify betel-quid chewers, alcohol- and tobacco-user controls, and healthy control.The results showed that logistic regression has a significant performance on identifying betel-quid chewers. The major advantage to this study is providing a 3D-autoencoder model and machine learning algorithm that can be used to discover the brain alternations in betel-quid chewers for clinical use in the future.

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