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Binary and Ternary Classifiers to Detect COVID-19 Patients Using Chest X-ray Images: An Efficient Layered CNN Approach

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Abstract

Coronavirus disease 2019, i.e., COVID-19, an emerging contagious disease with human-to-human transmission, first appeared at the end of year 2019. The sudden demand for disease diagnostic kits prompted researchers to shift their focus toward developing solutions that could assist in identifying COVID-19 using available resources. Therefore, it is imperative to develop a high-accuracy system that makes use of Artificial Intelligence and its tools considering its contribution to computer vision. The time consumed to diagnose test outcomes is to be taken care of as a crucial aspect of an efficient model. To address the global challenges faced by the COVID-19 pandemic, this study proposed two deep learning models developed for automatic COVID-19 detection and distinguish it from pneumonia, another common lung disease. The proposed designs implement layered convolutional neural networks and are trained on a data set of 1824 chest X-rays for binary classification (COVID-19 and normal) and 2736 chest X-rays for ternary classification (COVID-19, normal, and pneumonia). The input images and hyper-parameters in the convolution layers are fine-tuned during the model training phase. The observations show that the proposed models have achieved a better performance as compared to their earlier contemporaries’ approaches, resulting in accuracy, precision, recall, and F-score of 98.91%, 98.5%, 98.5%, and 99% for binary-class and 95.99%, 96.3%, 96%, and 96.33% for ternary-class classifiers, respectively. The presented architectures have been built from scratch, thus with the implemented convolutional layered architecture, they were successful in providing more efficient and early diagnosis of the disease.

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Data Availability

The dataset analyzed during the current study is publicly available at Mendeley Data (https://data.mendeley.com/datasets/2fxz4px6d8/4).

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Authors and Affiliations

  1. Delhi Skill and Entrepreneurship University, New Delhi, India

    Mamta Mittal

  2. Department of ECE, Indore Institute of Science and Technology, Indore, India

    Nitin Kumar Chauhan

  3. Software Engineer, Wealth and Personal Banking (WPB) IT, HSBC Technology India, Pune, India

    Adrija Ghansiyal

  4. Department of ECE, Karunya Institute of Technology and Sciences, Coimbatore, India

    D. Jude Hemanth

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  1. Mamta Mittal
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  2. Nitin Kumar Chauhan
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  4. D. Jude Hemanth
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Correspondence to D. Jude Hemanth.

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Mittal, M., Chauhan, N.K., Ghansiyal, A. et al. Binary and Ternary Classifiers to Detect COVID-19 Patients Using Chest X-ray Images: An Efficient Layered CNN Approach. New Gener. Comput. 42, 715–737 (2024). https://doi.org/10.1007/s00354-024-00254-5

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