JISE


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Journal of Information Science and Engineering, Vol. 38 No. 4, pp. 749-759


MedCheX: An Efficient COVID-19 Detection Model for Clinical Usage


CHI-SHIANG WANG1,*, FANG-YI SU2,* AND JUNG-HSIEN CHIANG3,+
Department of Computer Science and Information Engineering
National Cheng Kung University
Tainan, 701 Taiwan
E-mail: {hyaline0317
1; fangyi2}@iir.csie.ncku.edu.tw; jchiang@mail.ncku.edu.tw3


Due to the highly infectious and long incubation period of COVID-19, detecting COVID-19 efficiently and accurately is crucial since the epidemic outbreak. We proposed a new detection model based on U-Net++ and adopted dense blocks as the encoder. The model not only detects and classifies COVID-19 but also segment the lesion area precisely. We also designed a two-phase training strategy along with self-defined groups, especially the retrocardiac lesion to make model robust. We achieved 0.868 precision, 0.920 recall, and 0.893 F1-score on the COVID-19 open dataset. To contribute to this pandemic, we have set up a website with our model (https://medchex.tech/).


Keywords: COVID-19, convolutional neural network, computer vision, deep learning, medical image

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