Szczegóły publikacji
Opis bibliograficzny
SCovNet: a skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19 / Kiran Kumar Patro, Allam Jaya Prakash, Mohamed Hammad, Ryszard TADEUSIEWICZ, Paweł Pławiak // Biocybernetics and Biomedical Engineering ; ISSN 0208-5216. — 2023 — vol. 43 iss. 1, s. 352–368. — Bibliogr. s. 366–368, Abstr. — Publikacja dostępna online od: 2023-02-15
Autorzy (5)
- Patro Kiran Kumar
- Prakash Allam Jaya
- Hammad Mohamed
- AGHTadeusiewicz Ryszard
- Pławiak Paweł
Słowa kluczowe
Dane bibliometryczne
ID BaDAP | 145486 |
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Data dodania do BaDAP | 2023-03-02 |
Tekst źródłowy | URL |
DOI | 10.1016/j.bbe.2023.01.005 |
Rok publikacji | 2023 |
Typ publikacji | artykuł w czasopiśmie |
Otwarty dostęp | |
Czasopismo/seria | Biocybernetics and Biomedical Engineering |
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.