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المؤلفElharrouss, Omar
المؤلفSubramanian, Nandhini
المؤلفAlmaadeed, Noor
المؤلفAl-Maadeed, Somaya
تاريخ الإتاحة2020-10-22T06:07:19Z
تاريخ النشر2020
اسم المنشورQatar University Annual Research an Exhibition 2020 (quarfe)
معرّف المصادر الموحدhttps://doi.org/10.29117/quarfe.2020.0294
معرّف المصادر الموحدhttp://hdl.handle.net/10576/16518
الملخصThe novelty of the COVID-19 Disease and the speed of spread, that create a colossal chaos, impulse all the worldwide researchers to exploit all resources and capabilities to understand and analyze characteristics of the coronavirus in term of spread ways and virus incubation time. For that, the existing medical features like CT and X-ray images are used. For example, CT-scan images can be used for the detection of lung infection. But the challenges of these features such as the quality of the image and infection characteristics limitate 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. This poster proposes a multi-task deep-learning-based method for lung infection segmentation using CT-scan image.
اللغةen
الناشرQatar University Press
الموضوعCOVID-19
Lung Infection Segmentation
Deep learning
Encoder-decoder network
CNN
العنوانCOVID-19 Lung Infection Segmentation
النوعPoster
dc.accessType Open Access


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