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Impact of Wavelet Kernels on Predictive Capability of Radiomic Features: A Case Study on COVID-19 Chest X-ray Images

2023

Radiomic analysis allows for the detection of imaging biomarkers supporting decision-making processes in clinical environments, from diagnosis to prognosis. Frequently, the original set of radiomic features is augmented by considering high-level features, such as wavelet transforms. However, several wavelets families (so called kernels) are able to generate different multi-resolution representations of the original image, and which of them produces more salient images is not yet clear. In this study, an in-depth analysis is performed by comparing different wavelet kernels and by evaluating their impact on predictive capabilities of radiomic models. A dataset composed of 1589 chest X-ray ima…

chest X-ray imagesradiomic featuresSettore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioniwavelet kernelsRadiology Nuclear Medicine and imagingCOVID-19 prognosisComputer Vision and Pattern RecognitionElectrical and Electronic Engineeringmachine learning modelswavelet-derived featurespredictive capabilityComputer Graphics and Computer-Aided Design
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