Impact of Wavelet Kernels on Predictive Capability of Radiomic Features: A Case Study on COVID-19 Chest X-ray Images
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/9/2/32 |
| _version_ | 1797620118266576896 |
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| author | Francesco Prinzi Carmelo Militello Vincenzo Conti Salvatore Vitabile |
| author_facet | Francesco Prinzi Carmelo Militello Vincenzo Conti Salvatore Vitabile |
| author_sort | Francesco Prinzi |
| collection | DOAJ |
| description | 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 images was used for COVID-19 prognosis prediction as a case study. Random forest, support vector machine, and XGBoost were trained (on a subset of 1103 images) after a rigorous feature selection strategy to build-up the predictive models. Next, to evaluate the models generalization capability on unseen data, a test phase was performed (on a subset of 486 images). The experimental findings showed that <i>Bior1.5</i>, <i>Coif1</i>, <i>Haar</i>, and <i>Sym2</i> kernels guarantee better and similar performance for all three machine learning models considered. Support vector machine and random forest showed comparable performance, and they were better than XGBoost. Additionally, random forest proved to be the most stable model, ensuring an appropriate balance between sensitivity and specificity. |
| first_indexed | 2024-03-11T08:36:30Z |
| format | Article |
| id | doaj.art-50a2e7b1fc414e21a360405af5817332 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-11T08:36:30Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-50a2e7b1fc414e21a360405af58173322023-11-16T21:25:04ZengMDPI AGJournal of Imaging2313-433X2023-01-01923210.3390/jimaging9020032Impact of Wavelet Kernels on Predictive Capability of Radiomic Features: A Case Study on COVID-19 Chest X-ray ImagesFrancesco Prinzi0Carmelo Militello1Vincenzo Conti2Salvatore Vitabile3Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, 90127 Palermo, ItalyInstitute for High-Performance Computing and Networking, National Research Council (ICAR-CNR), 90146 Palermo, ItalyFaculty of Engineering and Architecture, University Kore of Enna, 94100 Enna, ItalyDepartment of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, 90127 Palermo, ItalyRadiomic 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 images was used for COVID-19 prognosis prediction as a case study. Random forest, support vector machine, and XGBoost were trained (on a subset of 1103 images) after a rigorous feature selection strategy to build-up the predictive models. Next, to evaluate the models generalization capability on unseen data, a test phase was performed (on a subset of 486 images). The experimental findings showed that <i>Bior1.5</i>, <i>Coif1</i>, <i>Haar</i>, and <i>Sym2</i> kernels guarantee better and similar performance for all three machine learning models considered. Support vector machine and random forest showed comparable performance, and they were better than XGBoost. Additionally, random forest proved to be the most stable model, ensuring an appropriate balance between sensitivity and specificity.https://www.mdpi.com/2313-433X/9/2/32radiomic featuresmachine learning modelswavelet kernelspredictive capabilitywavelet-derived featureschest X-ray images |
| spellingShingle | Francesco Prinzi Carmelo Militello Vincenzo Conti Salvatore Vitabile Impact of Wavelet Kernels on Predictive Capability of Radiomic Features: A Case Study on COVID-19 Chest X-ray Images Journal of Imaging radiomic features machine learning models wavelet kernels predictive capability wavelet-derived features chest X-ray images |
| title | Impact of Wavelet Kernels on Predictive Capability of Radiomic Features: A Case Study on COVID-19 Chest X-ray Images |
| title_full | Impact of Wavelet Kernels on Predictive Capability of Radiomic Features: A Case Study on COVID-19 Chest X-ray Images |
| title_fullStr | Impact of Wavelet Kernels on Predictive Capability of Radiomic Features: A Case Study on COVID-19 Chest X-ray Images |
| title_full_unstemmed | Impact of Wavelet Kernels on Predictive Capability of Radiomic Features: A Case Study on COVID-19 Chest X-ray Images |
| title_short | Impact of Wavelet Kernels on Predictive Capability of Radiomic Features: A Case Study on COVID-19 Chest X-ray Images |
| title_sort | impact of wavelet kernels on predictive capability of radiomic features a case study on covid 19 chest x ray images |
| topic | radiomic features machine learning models wavelet kernels predictive capability wavelet-derived features chest X-ray images |
| url | https://www.mdpi.com/2313-433X/9/2/32 |
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