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AuthorElharrouss, Omar
AuthorSubramanian, Nandhini
AuthorAl-Maadeed, Somaya
Available date2024-06-06T11:40:39Z
Publication Date2021-10-25
Publication NameSN Computer Science
Identifierhttp://dx.doi.org/10.1007/s42979-021-00874-4
CitationElharrouss, O., Subramanian, N., & Al-Maadeed, S. (2022). An encoder–decoder-based method for segmentation of COVID-19 lung infection in CT images. SN Computer Science, 3(1), 13.
ISSN2662-995X
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85161474740&origin=inward
URIhttp://hdl.handle.net/10576/55893
AbstractThe novelty of the COVID-19 Disease and the speed of spread, created colossal chaotic, impulse all the worldwide researchers to exploit all resources and capabilities to understand and analyze characteristics of the coronavirus in terms of spread ways and virus incubation time. For that, the existing medical features such as CT-scan and X-ray images are used. For example, CT-scan images can be used for the detection of lung infection. However, the quality of these images and infection characteristics limit the effectiveness of these features. Using artificial intelligence (AI) tools and computer vision algorithms, the accuracy of detection can be more accurate and can help to overcome these issues. In this paper, we propose a multi-task deep-learning-based method for lung infection segmentation on CT-scan images. Our proposed method starts by segmenting the lung regions that may be infected. Then, segmenting the infections in these regions. In addition, to perform a multi-class segmentation the proposed model is trained using the two-stream inputs. The multi-task learning used in this paper allows us to overcome the shortage of labeled data. In addition, the multi-input stream allows the model to learn from many features that can improve the results. To evaluate the proposed method, many metrics have been used including Sorensen–Dice similarity, Sensitivity, Specificity, Precision, and MAE metrics. As a result of experiments, the proposed method can segment lung infections with high performance even with the shortage of data and labeled images. In addition, comparing with the state-of-the-art method our method achieves good performance results. For example, the proposed method reached 78.6% for Dice, 71.1% for Sensitivity metric, 99.3% for Specificity 85.6% for Precision, and 0.062 for Mean Average Error metric, which demonstrates the effectiveness of the proposed method for lung infection segmentation.
SponsorThis publication was jointly supported by Qatar University ERG-250.
Languageen
PublisherSpringer Nature
SubjectCOVID-19
CT-scan image
Encoder–decoder network
Lung infection segmentation
TitleAn Encoder–Decoder-Based Method for Segmentation of COVID-19 Lung Infection in CT Images
TypeArticle
Issue Number1
Volume Number3
ESSN2661-8907
dc.accessType Full Text


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