P2P-COVID-GAN: Classification and Segmentation of COVID-19 Lung Infections From CT Images Using GAN

Nandhini Abirami, Durai Raj Vincent and Seifedine Kadry
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Nandhini Abirami: Vellore Institute of Technology, India
Durai Raj Vincent: Vellore Institute of Technology, India
Seifedine Kadry: Lebanese American University, Lebanon & Noroff University College, Norway

International Journal of Data Warehousing and Mining (IJDWM), 2021, vol. 17, issue 4, 101-118

Abstract: Early and automatic segmentation of lung infections from computed tomography images of COVID-19 patients is crucial for timely quarantine and effective treatment. However, automating the segmentation of lung infection from CT slices is challenging due to a lack of contrast between the normal and infected tissues. A CNN and GAN-based framework are presented to classify and then segment the lung infections automatically from COVID-19 lung CT slices. In this work, the authors propose a novel method named P2P-COVID-SEG to automatically classify COVID-19 and normal CT images and then segment COVID-19 lung infections from CT images using GAN. The proposed model outperformed the existing classification models with an accuracy of 98.10%. The segmentation results outperformed existing methods and achieved infection segmentation with accurate boundaries. The Dice coefficient achieved using GAN segmentation is 81.11%. The segmentation results demonstrate that the proposed model outperforms the existing models and achieves state-of-the-art performance.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jdwm00:v:17:y:2021:i:4:p:101-118

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