Abstract
The outbreak of COVID-19 has been striking the world for months and caused hundreds of thousands of mortality. Early and accurate detection turns out to be one of the most effective ways to slow the spreading of the virus. To help radiologists interpret images, we developed an automatic CT image-based detection system, which achieved high accuracy on the detection of COVID-19. The proposed model in the detection system is codenamed GoogLeNet-COD, which utilizes one of the state-of-the-art deep convolutional neural networks GooLeNet as the backbone. As GoogLeNet was initially trained on ImageNet, we first replaced the last top two layers with four new layers, which include the dropout layer, two fully-connected layers and the output layer. The dropout technique is utilized to prevent overfitting in the system by inserting a dropout layer in the network. The newly added fully-connected layer serves as a transitional layer that prevents significant information loss while the last fully-connected layer is used to generate possibilities for the final output layer. The hold-out validation method is used to evaluate the performance of the proposed system. The experiment on a private COVID-19 dataset showed a high accuracy of our system.
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Acknowledgment
The paper is partially supported by the Royal Society International Exchanges Cost Share Award, UK (RP202G0230), Medical Research Council Confidence in Concept Award, UK (MC_PC_17171), Hope Foundation for Cancer Research, UK (RM60G0680), and Guangxi Key Laboratory of Trusted Software (kx201901).
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Yu, X., Wang, SH., Zhang, X., Zhang, YD. (2020). Detection of COVID-19 by GoogLeNet-COD. In: Huang, DS., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12463. Springer, Cham. https://doi.org/10.1007/978-3-030-60799-9_43
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DOI: https://doi.org/10.1007/978-3-030-60799-9_43
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