Journal
INTERNATIONAL JOURNAL OF DATA WAREHOUSING AND MINING
Volume 17, Issue 4, Pages 101-118Publisher
IGI GLOBAL
DOI: 10.4018/IJDWM.2021100105
Keywords
CNN; Computer Vision; COVID-19; CT Scans; Deep Learning; GAN; Lung Segmentation; Pix2Pix
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The study proposes a CNN and GAN-based framework for automated classification and segmentation of lung infections in COVID-19 lung CT images. The proposed model outperforms existing methods in accuracy and segmentation results, achieving precise boundaries and high segmentation accuracy.
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.
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