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DEELE-Rad: exploiting deep radiomics features in deep learning models using COVID-19 chest X-ray images

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Abstract

Purpose

Deep learning-based radiomics techniques have the potential to aid specialists and physicians in performing decision-making in COVID-19 scenarios. Specifically, a Deep Learning (DL) ensemble model is employed to classify medical images when addressing the diagnosis during the classification tasks for COVID-19 using chest X-ray images. It also provides feasible and reliable visual explicability concerning the results to support decision-making.

Methods

Our DEELE-Rad approach integrates DL and Machine Learning (ML) techniques. We use deep learning models to extract deep radiomics features and evaluate its performance regarding end-to-end classifiers. We avoid successive radiomics approach steps by employing these models with transfer learning techniques from ImageNet, such as VGG16, ResNet50V2, and DenseNet201 architectures. We extract 100 and 500 deep radiomics features from each DL model. We also placed these features into well-established ML classifiers and applied automatic parameter tuning and a cross-validation strategy. Besides, we exploit insights into the decision-making behavior by applying a visual explanation method.

Results

Experimental evaluation on our proposed approach achieved 89.97% AUC when using 500 deep radiomics features from the DenseNet201 end-to-end classifier. Besides, our ensemble DEELE-Rad method improves the results up to 96.19% AUC for the 500 dimensions. To outperform, ML DEELE-Rad reached the best results with an Accuracy of 98.39% and 99.19% AUC for the same setup. Our visual assessment employs additional possibilities for specialists and physicians to decision-making.

Conclusion

The results reflect that the DEELE-Rad approach provides robustness and confidence to the images’ analysis. Our approach can benefit healthcare specialists when employed at clinical routines and respective decision-making procedures. For reproducibility, our code is available at https://github.com/usmarcv/deele-rad.

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Code availability

Our code available at https://github.com/usmarcv/deele-rad

Notes

  1. https://keras.io/api/applications/

  2. https://keras.io/api/optimizers/adam/

  3. https://keras.io/api/callbacks/early_stopping/

  4. https://sklearn.preprocessing.StandardScaler.html2

  5. https://anaconda.org/anaconda/cudatoolkit

  6. https://scikit-learn.org/stable/index.html

  7. https://matplotlib.org/

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Acknowledgements

This study was financed in part by the São Paulo Research Foundation (FAPESP – grants 2016/17078-0, 2020/07200-9, 2021/08982-3, 2023/14759-0, 2023/14390-7 and 2024/09462-1), National Council for Scientific and Technological Development (CNPq – grants 152760/2021-0, 308544/2021-8, and 308738/2021-7), and Coordination for Higher Education Personnel Improvement (Finance Code 001 and grant 88887.969051/2024-00).

Funding

Coordination for Higher Education Personnel Improvement (CAPES), National Council for Scientific and Technological Development (CNPq), and São Paulo Research Foundation (FAPESP).

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Authors and Affiliations

  1. Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, São Paulo, 13566-590, Brazil

    Márcus V. L. Costa, Erikson J. de Aguiar, Lucas S. Rodrigues, Caetano Traina Jr. & Agma J. M. Traina

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  1. Márcus V. L. Costa
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Correspondence to Márcus V. L. Costa.

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Costa, M.V.L., de Aguiar, E.J., Rodrigues, L.S. et al. DEELE-Rad: exploiting deep radiomics features in deep learning models using COVID-19 chest X-ray images. Health Inf Sci Syst 13, 11 (2025). https://doi.org/10.1007/s13755-024-00330-6

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