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Journal Article Ppublish 29 citations

PubMed: 36819118 DOI: 10.1016/j.bbe.2023.01.005

SCovNet: A skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19.

Biocybern Biomed Eng 43(1):352-368

Authors: Kiran Kumar Patro, Jaya Prakash Allam, Mohamed Hammad, Ryszard Tadeusiewicz, Paweł Pławiak

Affiliations

  • Department of ECE, Aditya Institute of Technology and Management, Tekkali AP-532201, India.
  • Department of EC, National Institute of Technology Rourkela, Rourkela, Odisha 769008, India.
  • Information Technology Dept. Faculty of Computers and Information, Menoufia University, Menoufia, Egypt.
  • Department of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, Krakow, Poland.
  • Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warszawska 24, 31-155 Krakow, Poland.
  • Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, 44-100 Gliwice, Poland.

Abstract

Background and Objective

The global population has been heavily impacted by the COVID-19 pandemic of coronavirus. Infections are spreading quickly around the world, and new spikes (Delta, Delta Plus, and Omicron) are still being made. The real-time reverse transcription-polymerase chain reaction (RT-PCR) is the method most often used to find viral RNA in a nasopharyngeal swab. However, these diagnostic approaches require human involvement and consume more time per prediction. Moreover, the existing conventional test mainly suffers from false negatives, so there is a chance for the virus to spread quickly. Therefore, a rapid and early diagnosis of COVID-19 patients is needed to overcome these problems.

Methods

Existing approaches based on deep learning for COVID detection are suffering from unbalanced datasets, poor performance, and gradient vanishing problems. A customized skip connection-based network with a feature union approach has been developed in this work to overcome some of the issues mentioned above. Gradient information from chest X-ray (CXR) images to subsequent layers is bypassed through skip connections. In the script's title, "SCovNet" refers to a skip-connection-based feature union network for detecting COVID-19 in a short notation. The performance of the proposed model was tested with two publicly available CXR image databases, including balanced and unbalanced datasets.

Results

A modified skip connection-based CNN model was suggested for a small unbalanced dataset (Kaggle) and achieved remarkable performance. In addition, the proposed model was also tested with a large GitHub database of CXR images and obtained an overall best accuracy of 98.67% with an impressive low false-negative rate of 0.0074.

Conclusions

The results of the experiments show that the proposed method works better than current methods at finding early signs of COVID-19. As an additional point of interest, we must mention the innovative hierarchical classification strategy provided for this work, which considered both balanced and unbalanced datasets to get the best COVID-19 identification rate.

MeSH

Polymerase Chain Reaction, Covid-19, Deep Learning, Work, Viruses

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