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

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

Identyfikatory

DOI

10.1016/j.bbe.2023.01.005

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

COVID-19 detection; CNN; deep learning; image augmentation; skip connection; X-ray image

PL

wykrywanie COVID-19; CNN; uczenie głębokie; augmentacja obrazu; zdjęcie rentgenowskie

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2023
• Tom: Vol. 43, no. 1
• Strony: 352--368
• Opis fizyczny: Bibliogr. 66 poz., rys., tab., wykr.

Twórcy

autor

Patro Kiran Kumar

• Department of ECE, Aditya Institute of Technology and Management, Tekkali AP-532201, India

autor

Allam Jaya Prakash

• Department of EC, National Institute of Technology Rourkela, Rourkela, Odisha, India

autor

Hammad Mohamed

• Information Technology Dept., Faculty of Computers and Information, Menoufia University, Menoufia, Egypt

autor

Tadeusiewicz Ryszard

• Department of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, Krakow, Poland

autor

Pławiak Paweł

• Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Krakow, Poland
• Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Gliwice, Poland

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)