TY - JOUR
T1 - MedCheX: An Efficient COVID-19 Detection Model for Clinical Usage
AU - WANG, CHI-SHIANG
AU - SU, FANG-YI
AU - CHIANG, JUNG-HSIEN
PY - 2022/7/1
Y1 - 2022/7/1
N2 - 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/).
AB - 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/).
U2 - 10.6688/JISE.202207_38(4).0003
DO - 10.6688/JISE.202207_38(4).0003
M3 - Article
SN - 1016-2364
VL - 38
SP - 749
EP - 759
JO - Journal of Information Science and Engineering
JF - Journal of Information Science and Engineering
IS - 4
ER -