Risk of data leakage in estimating the diagnostic performance of a deep-learning-based computer-aided system for psychiatric disorders.

ABSTRACT: Deep-learning approaches with data augmentation have been widely used when developing neuroimaging-based computer-aided diagnosis (CAD) systems. To prevent the inflated diagnostic performance caused by data leakage, a correct cross-validation (CV) method should be employed, but this has been still overlooked in recent deep-learning-based CAD studies. The goal of this study was to investigate the impact of correct and incorrect CV methods on the diagnostic performance of deep-learning-based CAD systems after data augmentation. To this end, resting-state electroencephalogram (EEG) data recorded from post-traumatic stress disorder patients and healthy controls were augmented using a cropping method with different window sizes, respectively. Four different CV approaches were used to estimate t

SUBMITTER: Lee HT 

PROVIDER: S-EPMC10547830 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature