Auxiliary Diagnosis Algorithm for Pediatric Pneumonia Based on Deep Residual Network
ZHANG Ke, ZHANG Chunxiao
Medical Engineering Management Office, Shandong Provincial Hospital Affiliated to Shandong First Medical University (Shandong
Provincial Hospital), Jinan Shandong 250021, China
Abstract：Objective To propose an improved convolutional neural network model for enriching the auxiliary diagnosis algorithm of
pediatric pneumonia and improving the efficiency and quality of X-ray image analysis of pediatric pneumonia. Methods Based on
deep residual network (RESNext-50) and SE module was fused to establish the association between channels. Then, the Leaky ReLU
activation function was used to replace the ReLU activation function in the model construction process, and group normalization
was used as normalization method. Finally, the pre-trained model was trained and tested on Chest X-Ray dataset, and accuracy,
recall and precision were used as evaluation indexes. Results The recognition accuracy, precision and recall of the network model
reached 91.19%, 89.70% and 91.39%, respectively. Conclusion The network model has a certain practicality, which can better fit the
pneumonia image data set, effectively improve the accuracy of pediatric pneumonia image classification, and can be used as a new
method for clinical diagnosis of pediatric pneumonia.
张科，张春晓. 基于深度残差网络的儿科肺炎辅助诊断算法[J]. 中国医疗设备, 2022, 37(9): 42-46.
ZHANG Ke, ZHANG Chunxiao. Auxiliary Diagnosis Algorithm for Pediatric Pneumonia Based on Deep Residual Network. China Medical Devices, 2022, 37(9): 42-46.
