A radiomics-boosted deep-learning model for COVID-19 and non-COVID-19 pneumonia classification using chest x-ray images
© 2022 American Association of Physicists in Medicine..
PURPOSE: To develop a deep learning model design that integrates radiomics analysis for enhanced performance of COVID-19 and non-COVID-19 pneumonia detection using chest x-ray images.
METHODS: As a novel radiomics approach, a 2D sliding kernel was implemented to map the impulse response of radiomic features throughout the entire chest x-ray image; thus, each feature is rendered as a 2D map in the same dimension as the x-ray image. Based on each of the three investigated deep neural network architectures, including VGG-16, VGG-19, and DenseNet-121, a pilot model was trained using x-ray images only. Subsequently, two radiomic feature maps (RFMs) were selected based on cross-correlation analysis in reference to the pilot model saliency map results. The radiomics-boosted model was then trained based on the same deep neural network architecture using x-ray images plus the selected RFMs as input. The proposed radiomics-boosted design was developed using 812 chest x-ray images with 262/288/262 COVID-19/non-COVID-19 pneumonia/healthy cases, and 649/163 cases were assigned as training-validation/independent test sets. For each model, 50 runs were trained with random assignments of training/validation cases following the 7:1 ratio in the training-validation set. Sensitivity, specificity, accuracy, and ROC curves together with area-under-the-curve (AUC) from all three deep neural network architectures were evaluated.
RESULTS: After radiomics-boosted implementation, all three investigated deep neural network architectures demonstrated improved sensitivity, specificity, accuracy, and ROC AUC results in COVID-19 and healthy individual classifications. VGG-16 showed the largest improvement in COVID-19 classification ROC (AUC from 0.963 to 0.993), and DenseNet-121 showed the largest improvement in healthy individual classification ROC (AUC from 0.962 to 0.989). The reduced variations suggested improved robustness of the model to data partition. For the challenging non-COVID-19 pneumonia classification task, radiomics-boosted implementation of VGG-16 (AUC from 0.918 to 0.969) and VGG-19 (AUC from 0.964 to 0.970) improved ROC results, while DenseNet-121 showed a slight yet insignificant ROC performance reduction (AUC from 0.963 to 0.949). The achieved highest accuracy of COVID-19/non-COVID-19 pneumonia/healthy individual classifications were 0.973 (VGG-19)/0.936 (VGG-19)/ 0.933 (VGG-16), respectively.
CONCLUSIONS: The inclusion of radiomic analysis in deep learning model design improved the performance and robustness of COVID-19/non-COVID-19 pneumonia/healthy individual classification, which holds great potential for clinical applications in the COVID-19 pandemic.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2022 |
|---|---|
| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:49 |
|---|---|
| Enthalten in: |
Medical physics - 49(2022), 5 vom: 01. Mai, Seite 3213-3222 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Hu, Zongsheng [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
|---|
| Anmerkungen: |
Date Completed 11.05.2022 Date Revised 16.07.2022 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1002/mp.15582 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM337943362 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM337943362 | ||
| 003 | DE-627 | ||
| 005 | 20231225235548.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2022 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1002/mp.15582 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1126.xml |
| 035 | |a (DE-627)NLM337943362 | ||
| 035 | |a (NLM)35263458 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Hu, Zongsheng |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A radiomics-boosted deep-learning model for COVID-19 and non-COVID-19 pneumonia classification using chest x-ray images |
| 264 | 1 | |c 2022 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 11.05.2022 | ||
| 500 | |a Date Revised 16.07.2022 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2022 American Association of Physicists in Medicine. | ||
| 520 | |a PURPOSE: To develop a deep learning model design that integrates radiomics analysis for enhanced performance of COVID-19 and non-COVID-19 pneumonia detection using chest x-ray images | ||
| 520 | |a METHODS: As a novel radiomics approach, a 2D sliding kernel was implemented to map the impulse response of radiomic features throughout the entire chest x-ray image; thus, each feature is rendered as a 2D map in the same dimension as the x-ray image. Based on each of the three investigated deep neural network architectures, including VGG-16, VGG-19, and DenseNet-121, a pilot model was trained using x-ray images only. Subsequently, two radiomic feature maps (RFMs) were selected based on cross-correlation analysis in reference to the pilot model saliency map results. The radiomics-boosted model was then trained based on the same deep neural network architecture using x-ray images plus the selected RFMs as input. The proposed radiomics-boosted design was developed using 812 chest x-ray images with 262/288/262 COVID-19/non-COVID-19 pneumonia/healthy cases, and 649/163 cases were assigned as training-validation/independent test sets. For each model, 50 runs were trained with random assignments of training/validation cases following the 7:1 ratio in the training-validation set. Sensitivity, specificity, accuracy, and ROC curves together with area-under-the-curve (AUC) from all three deep neural network architectures were evaluated | ||
| 520 | |a RESULTS: After radiomics-boosted implementation, all three investigated deep neural network architectures demonstrated improved sensitivity, specificity, accuracy, and ROC AUC results in COVID-19 and healthy individual classifications. VGG-16 showed the largest improvement in COVID-19 classification ROC (AUC from 0.963 to 0.993), and DenseNet-121 showed the largest improvement in healthy individual classification ROC (AUC from 0.962 to 0.989). The reduced variations suggested improved robustness of the model to data partition. For the challenging non-COVID-19 pneumonia classification task, radiomics-boosted implementation of VGG-16 (AUC from 0.918 to 0.969) and VGG-19 (AUC from 0.964 to 0.970) improved ROC results, while DenseNet-121 showed a slight yet insignificant ROC performance reduction (AUC from 0.963 to 0.949). The achieved highest accuracy of COVID-19/non-COVID-19 pneumonia/healthy individual classifications were 0.973 (VGG-19)/0.936 (VGG-19)/ 0.933 (VGG-16), respectively | ||
| 520 | |a CONCLUSIONS: The inclusion of radiomic analysis in deep learning model design improved the performance and robustness of COVID-19/non-COVID-19 pneumonia/healthy individual classification, which holds great potential for clinical applications in the COVID-19 pandemic | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a deep learning | |
| 650 | 4 | |a radiomics | |
| 650 | 4 | |a x-ray | |
| 700 | 1 | |a Yang, Zhenyu |e verfasserin |4 aut | |
| 700 | 1 | |a Lafata, Kyle J |e verfasserin |4 aut | |
| 700 | 1 | |a Yin, Fang-Fang |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Chunhao |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Medical physics |d 1974 |g 49(2022), 5 vom: 01. Mai, Seite 3213-3222 |w (DE-627)NLM000294748 |x 2473-4209 |7 nnns |
| 773 | 1 | 8 | |g volume:49 |g year:2022 |g number:5 |g day:01 |g month:05 |g pages:3213-3222 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1002/mp.15582 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 49 |j 2022 |e 5 |b 01 |c 05 |h 3213-3222 | ||