Deep Features from Pretrained Networks Do Not Outperform Hand-Crafted Features in Radiomics.

ABSTRACT: In radiomics, utilizing features extracted from pretrained deep networks could result in models with a higher predictive performance than those relying on hand-crafted features. This study compared the predictive performance of models trained with either deep features, hand-crafted features, or a combination of these features in terms of the area under the receiver-operating characteristic curve (AUC) and other metrics. We trained models on ten radiological datasets using five feature selection methods and three classifiers. Our results indicate that models based on deep features did not show an improved AUC compared to those utilizing hand-crafted features (deep: AUC 0.775, hand-crafted: AUC 0.789; p = 0.28). Including morphological features alongside deep features led to overall i

SUBMITTER: Demircioglu A 

PROVIDER: S-EPMC10606594 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature