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Discrimination of smoking status by MRI based on deep learning method

  
@article{QIMS22979,
	author = {Shuangkun Wang and Rongguo Zhang and Yufeng Deng and Kuan Chen and Dan Xiao and Peng Peng and Tao Jiang},
	title = {Discrimination of smoking status by MRI based on deep learning method},
	journal = {Quantitative Imaging in Medicine and Surgery},
	volume = {8},
	number = {11},
	year = {2018},
	keywords = {},
	abstract = {Background: This study aimed to assess the feasibility of deep learning-based magnetic resonance imaging (MRI) in the prediction of smoking status. 
Methods: The head MRI 3D-T1WI images of 127 subjects (61 smokers and 66 non-smokers) were collected, and 176 image slices obtained for each subject. These subjects were 23–45 years old, and the smokers had at least 5 years of smoking experience. Approximate 25% of the subjects were randomly selected as the test set (15 smokers and 16 non-smokers), and the remaining subjects as the training set. Two deep learning models were developed: deep 3D convolutional neural network (Conv3D) and convolution neural network plus a recurrent neural network (RNN) with long short-term memory architecture (ConvLSTM). 
Results: In the prediction of smoking status, Conv3D model achieved an accuracy of 80.6% (25/31), a sensitivity of 80.0% and a specificity of 81.3%, and ConvLSTM model achieved an accuracy of 93.5% (29/31), a sensitivity of 93.33% and a specificity of 93.75%. The accuracy obtained by these methods was significantly higher than that (},
	url = {http://qims.amegroups.com/article/view/22979}
}