Affiliation:
1. School of Aerospace Engineering, Xiamen University, Xiangan South Road, Xiamen 361102, China
2. Chengyi University College, Jimei University, 199 Jimei Avenue, Xiamen 361021, China
Abstract
In order to solve the problem of existing diagnostic methods for chronic gastritis which are complex and traumatic, a novel noninvasive method for diagnosis of chronic gastric based on e-nose and deep convolutional neural network is proposed. Firstly, in order to collect samples, a respiratory gas sampling device was established and the response curve of respiratory gas is generated. Then, a deep convolutional neural network for the diagnosis of chronic gastritis is proposed to recognize and classify the respiratory gas response curve. The DCNN model attained good results with accuracy, sensitivity, and specificity of 85.00%, 90.00%, and 80.00%, respectively, for chronic gastric prediction. The proposed method provides a new way for the clinical auxiliary diagnoses of chronic gastric.
Subject
Electrical and Electronic Engineering,Instrumentation,Control and Systems Engineering
Cited by
5 articles.
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