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
A convolutional neural network (CNN)-based autoencoder model and logistic regression (LR) reached the highest accuracy on classifying betel-quid chewers, tobacco- and alcohol-user controls, and healthy controls mutually exclusive using rs-fMRI as input features.