Impact of retraining and data partitions on the generalizability of a deep learning model in the task of COVID-19 classification on chest radiographs

Abstract

Purpose: This study aimed to investigate the impact of different model retraining schemes and data partitioning on model performance in the task of COVID-19 classification on standard chest radiographs (CXRs), in the context of model generalizability. Approach: regularization, and (4) repartition of the training set from Set A 200 times and report AUC values. Results: on repartitions of Set A. The lowest AUC value (0.66 [0.62, 0.69]) of the Set A repartitions was no longer significantly different from the initial 0.67 achieved on Set B. Conclusions: Different data repartitions of the same dataset used to train a DL model demonstrated significantly different performance values that helped explain the discrepancy between Set A and Set B and further demonstrated the limitations of model generalizability.

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