The COVID-19 medical diagnosis method based on individual’s chest X-ray (CXR) is achieved difficultly
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.
BU Ran (卜冉), XIANG Wei∗ (向伟), CAO Shitong (曹世同)
. COVID-19 Interpretable Diagnosis Algorithm Based on a Small Number of Chest X-Ray Samples[J]. Journal of Shanghai Jiaotong University(Science), 2022
, 27(1)
: 81
-89
.
DOI: 10.1007/s12204-021-2393-2
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