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Multimedia Systems
Erschienen in: Multimedia Systems 4/2023

19.04.2023 | Regular Paper

COVID-SegNet: encoder–decoder-based architecture for COVID-19 lesion segmentation in chest X-ray

verfasst von: Tarun Agrawal, Prakash Choudhary

Erschienen in: Multimedia Systems | Ausgabe 4/2023

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Abstract

The coronavirus disease 2019, initially named 2019-nCOV (COVID-19) has been declared a global pandemic by the World Health Organization in March 2020. Because of the growing number of COVID patients, the world’s health infrastructure has collapsed, and computer-aided diagnosis has become a necessity. Most of the models proposed for the COVID-19 detection in chest X-rays do image-level analysis. These models do not identify the infected region in the images for an accurate and precise diagnosis. The lesion segmentation will help the medical experts to identify the infected region in the lungs. Therefore, in this paper, a UNet-based encoder–decoder architecture is proposed for the COVID-19 lesion segmentation in chest X-rays. To improve performance, the proposed model employs an attention mechanism and a convolution-based atrous spatial pyramid pooling module. The proposed model obtained 0.8325 and 0.7132 values of the dice similarity coefficient and jaccard index, respectively, and outperformed the state-of-the-art UNet model. An ablation study has been performed to highlight the contribution of the attention mechanism and small dilation rates in the atrous spatial pyramid pooling module.
Vorheriger Artikel Multimodal image enhancement using convolutional sparse coding
Nächster Artikel Residual guided coordinate attention for selection channel aware image steganalysis

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Metadaten
Titel
COVID-SegNet: encoder–decoder-based architecture for COVID-19 lesion segmentation in chest X-ray
verfasst von
Tarun Agrawal
Prakash Choudhary
Publikationsdatum
19.04.2023
Verlag
Springer Berlin Heidelberg
Erschienen in
Multimedia Systems / Ausgabe 4/2023
Print ISSN: 0942-4962
Elektronische ISSN: 1432-1882
DOI
https://doi.org/10.1007/s00530-023-01096-9

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