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COVID-19 Classification on Chest X-ray Images Using Deep Learning Methods

Author

Listed:
  • Marios Constantinou

    (Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49132 Corfu, Greece)

  • Themis Exarchos

    (Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49132 Corfu, Greece)

  • Aristidis G. Vrahatis

    (Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49132 Corfu, Greece)

  • Panagiotis Vlamos

    (Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49132 Corfu, Greece)

Abstract

Since December 2019, the coronavirus disease has significantly affected millions of people. Given the effect this disease has on the pulmonary systems of humans, there is a need for chest radiographic imaging (CXR) for monitoring the disease and preventing further deaths. Several studies have been shown that Deep Learning models can achieve promising results for COVID-19 diagnosis towards the CXR perspective. In this study, five deep learning models were analyzed and evaluated with the aim of identifying COVID-19 from chest X-ray images. The scope of this study is to highlight the significance and potential of individual deep learning models in COVID-19 CXR images. More specifically, we utilized the ResNet50, ResNet101, DenseNet121, DenseNet169 and InceptionV3 using Transfer Learning. All models were trained and validated on the largest publicly available repository for COVID-19 CXR images. Furthermore, they were evaluated on unknown data that was not used for training or validation, authenticating their performance and clarifying their usage in a medical scenario. All models achieved satisfactory performance where ResNet101 was the superior model achieving 96% in Precision, Recall and Accuracy, respectively. Our outcomes show the potential of deep learning models on COVID-19 medical offering a promising way for the deeper understanding of COVID-19.

Suggested Citation

  • Marios Constantinou & Themis Exarchos & Aristidis G. Vrahatis & Panagiotis Vlamos, 2023. "COVID-19 Classification on Chest X-ray Images Using Deep Learning Methods," IJERPH, MDPI, vol. 20(3), pages 1-13, January.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:3:p:2035-:d:1044090
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    References listed on IDEAS

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    2. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 542(7639), pages 115-118, February.
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