A Novel Deep Convolution Neural Network Model for CT Image Classification Based on COVID-19

Abstract

Since the outbreak of novel coronavirus pneumonia (COVID-19) in 2019, normal learning and living have been severely affected, and human life and health have been seriously threatened. Therefore, it is crucial to diagnose the novel coronavirus pneumonia rapidly and efficiently. In this study, based on the classical image classification neural network model, a novel deep convolutional neural network model based on the attention mechanism is proposed and named the LACNN_CBAM model. The accuracy Acc, precision Pre, recall Rec and F-1 scores of the model in the public dataset collated from published papers are 0.989, 0.992, 0.992, and 0.992, which are respectively higher than existing learning models. The model determines whether a patient has COVID-19 and community-acquired pneumonia by patient’s CT images. The effectiveness of the model was demonstrated by experimental results on a clinical dataset. We believe that the model proposed in this paper can help physicians to diagnose COVID-19 and community-acquired pneumonia efficiently and accurately in reality.