典型文献
COVID-19 Interpretable Diagnosis Algorithm Based on a Small Number of Chest X-Ray Samples
文献摘要:
The COVID-19 medical diagnosis method based on individual's chest X-ray (CXR) is achieved dif-ficultly in the initial research,owing to difficulties in identifying CXR data of COVID-19 individuals.At the beginning of the study,infected individuals' CXRs were scarce.The combination of artificial intelligence (AI)and medical diagnosis has been advanced and popular.To solve the difficulties,the interpretability analysis of AI model was used to explore the pathological characteristics of CXR samples infected with COVID-19 and assist in medical diagnosis.The dataset was expanded by data augmentation to avoid overfitting.Transfer learning was used to test different pre-trained models and the unique output layers were designed to complete the model training with few samples.In this study,the output results of four pre-trained models in three different output layers were compared,and the results after data augmentation were compared with the results of the original dataset.The control variable method was used to conduct independent tests of 24 groups.Finally,99.23% accuracy and 98%recall rate were obtained,and the visual results of CXR interpretability analysis were displayed.The network of COVID-19 interpretable diagnosis algorithm has the characteristics of high generalization and lightweight.It can be quickly applied to other urgent tasks with insufficient experimental data.At the same time,interpretability analysis brings new possibilities for medical diagnosis.
文献关键词:
中图分类号:
作者姓名:
BU Ran;XIANG Wei;CAO Shitong
作者机构:
Key Laboratory of Electronic and Information Engineering,State Ethnic Affairs Commission (Southwest Minzu University),Chengdu 610041,China
文献出处:
引用格式:
[1]BU Ran;XIANG Wei;CAO Shitong-.COVID-19 Interpretable Diagnosis Algorithm Based on a Small Number of Chest X-Ray Samples)[J].上海交通大学学报(英文版),2022(01):81-89
A类:
ficultly,CXRs
B类:
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AB值:
0.523532
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