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COVID-19 classification in X-ray/CT images using pretrained deep learning schemes

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Abstract

Computer-aided diagnosis (CAD) techniques, exemplified by chest x-ray (CXR)-based methods, offer a cost-effective alternative for early-stage COVID-19 diagnosis compared to expensive options such as polymerase chain reaction (PCR) and computed tomography (CT) scan. Despite efforts to diagnose COVID-19 with CXR-based methods, their performance could be improved by considering the spatial relationships between regions of interest (ROIs) in CXR images. This oversight hinders the ability to accurately identify areas of the human lung most vulnerable to COVID-19. This model implements a two-way classification system to differentiate between lung X-ray impressions, accurately determining whether they are affected or normal. The effectiveness of this system is assessed using metrics such as accuracy, recall, precision, and F1-score. We employed over 2409 samples of X-ray images in the COVID-19 diagnosis process. The results obtained from the VGG16 model showcase outstanding performance, with a recognition rate of 99.58% for X-ray images and 94.29% for CT-scan pictures within the given sample size and two-class categorization. This model surpasses all existing approaches documented in the literature. Medical professionals and healthcare workers can effectively utilize this proposed system, leveraging X-rays and CT scans of human lungs to identify COVID-19 cases accurately.

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Data Availability

Data set1. Chest X-Ray Images: https://www.kaggle.com/dataets/datasets/paultimothymooney/chest-xraypneumonia. Accessed 01 Jan 2024.

Dataset 2. Covid CT scan: https://www.kaggle.com/datasets/maedemaftouni/large-covid19-ct-slice-dataset. Accessed 01 Jan 2024.

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Acknowledgements

The authors express their sincere appreciation to Dr. V. Ra**ikanth for his valuable insights and unwavering support during the course of this research. His contributions have significantly enriched the quality of this study.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, Tamil Nadu, 602105, India

    Narenthira Kumar Appavu & Nelson Kennedy Babu C

  2. Department of Applied Data Science, Noroff University College, Kristiansand, Norway

    Seifedine Kadry

  3. Artificial Intelligence Research Center (AIRC), Ajman University, 346, Ajman, United Arab Emirates

    Seifedine Kadry

  4. Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon

    Seifedine Kadry

  5. MEU Research Unit, Middle East University, Amman, 11831, Jordan

    Seifedine Kadry

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  1. Narenthira Kumar Appavu
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Correspondence to Narenthira Kumar Appavu.

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Appavu, N., Babu C, N.K. & Kadry, S. COVID-19 classification in X-ray/CT images using pretrained deep learning schemes. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18721-y

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