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Identification of COVID-19 with CT scans using radiomics and DL-based features

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Abstract

Deep learning plays a crucial role in identifying COVID-19 patients from computed tomography (CT) scans by leveraging its ability to analyze vast amounts of image data and extract patterns indicative of the disease. While deep learning-based models have consistently achieved state-of-the-art performance, the incorporation of relevant handcrafted features alongside deep learning-based features has the potential to enhance overall performance even further. Therefore, this paper proposes a hybrid approach that combines handcrafted and deep learning features from CT scan images for accurate COVID-19 classification. Handcrafted features capturing image statistics are derived through radiomics, while deep learning features are extracted using the Xception model. Preprocessing techniques like binary thresholding and segmentation are used to remove noises and locate the proper diseased area to enhance COVID-19 diagnosis. The approach is evaluated on a dataset of 2482 CT scan images and outperforms state-of-the-art techniques with an accuracy of 0.98, a positive predictive value (PPV) of 0.99, sensitivity of 0.99, specificity of 0.98, and an \(F_1\)-score of 0.99. The combined use of radiomics and deep learning features can make it a promising tool for COVID-19 diagnosis and monitoring, offering support for clinical decision-making and potentially benefiting other respiratory diseases.

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Data availability

The dataset for the current study is available at: www.kaggle.com/plameneduardo/sarscov2-ctscan-dataset and unseen dataset is available at : https://github.com/UCSD-AI4H/COVID-CT.

Notes

  1. www.kaggle.com/plameneduardo/sarscov2-ctscan-dataset.

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Acknowledgements

We would like to extend our heartfelt gratitude to Dr. Vikash Kumar Raj, Senior Medical Officer (SMO) at the National Institute of Technology Patna, for his invaluable input and guidance throughout our study. His expertise and insights have played a crucial role in shaping our approach and addressing various issues.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Patna, Bihar, 800005, India

    Sunil Dalal & Jyoti Prakash Singh

  2. Department of Computer Science and Engineering, Kamla Nehru Institute Of Technology, Sultanpur, Uttar Pradesh, 228118, India

    Arvind Kumar Tiwari

  3. Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology, Prayagraj, Uttar Pradesh, India

    Abhinav Kumar

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  1. Sunil Dalal
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Correspondence to Jyoti Prakash Singh.

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Dalal, S., Singh, J.P., Tiwari, A.K. et al. Identification of COVID-19 with CT scans using radiomics and DL-based features. Netw Model Anal Health Inform Bioinforma 13, 14 (2024). https://doi.org/10.1007/s13721-024-00448-3

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