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Abstract #2689

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

Hsin-An Shen1, Ming-Chou Ho2,3, and Jun-Cheng Weng1,4,5
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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