An Early Diagnosis Method of COVID-19 Infection Based on ResNeXt and Improved nnU-Net

doi:10.3969/j.issn.1006-2475.2023.06.004

Abstract

Abstract: The early infection of novel coronavirus pneumonia is characterized by increased lung turbidity and density. In order to solve the problem of difficulty in diagnosing and locating lung lesions in early patients with computed tomography, an experimental protocol for the diagnosis of COVID-19 （Corona Virus Disease 2019） with lung lesion segmentation by ResNeXt and a modified nnU-Net （no-new-Net） is proposed. The mean accuracy of ResNeXt model classification is 0.8554, the AUC area is 0.8951, the Precision is 0.8321, the F1 score is 0.8132, and the mean Dice coefficient of improved nnU-Net model lesion segmentation reaches 0.7663, which is a combined improvement of 16.4% compared with other models segmentation ability. The experimental results show that this scheme can enhance the ability to extract infection features from the early lung CT images of new crowns, and achieve efficient disease typing and accurate lesion segmentation.

Key words: ResNeXt, improved nnU-Net, early diagnosis of neoconiosis, lung segmentation

CLC Number:

TP391.4

XU Hao, TIAN Zhen-yu, LI Chao-fan, CUI Xin-xin, YANG Jian-lan. An Early Diagnosis Method of COVID-19 Infection Based on ResNeXt and Improved nnU-Net[J]. Computer and Modernization, 2023, 0(06): 21-26.

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