Abstract: In order to solve the problem of computer aided diagnosis of novel coronavirus pneumonia (Covid-19: Corona virus disease 2019), a bilinear convolutional neural network model is created and a feature extraction subnetwork with VGG(Visual Geometry Group network) 16 and VGG19 is employed. The algorithm is applied to COVID-19 image classification and compared with the basic image classification algorithm. The results and lesion visualization analyses demonstrate that the bilinear convolutional neural network model outperforms other deep learning network models in terms of accuracy, with an accuracy of 95. 19% . By replacing softmaxlayer and using SVM(Support Vector Machines) classifier, the model classification accuracy is improved to 96. 78% . The study provides a trustworthy tool for the quick and accurate diagnosis and treatment of neonatal pneumonia and a confirmation of the viability of fine-grained imaging algorithms for the categorization of COVID-19 CT images. 

Key words: image classification, bilinear convolutional neural network, support vector machines ( SVM), Corona Virus Disease 2019(COVID-19)