# Lung's Segmentation Using Context-Aware Regressive Conditional GAN
###### Abstract  
After declaring COVID-19 pneumonia as a pandemic, researchers promptly advanced to seek solutions for patients fighting this fatal disease. Computed tomography (CT) scans offer valuable insight into how COVID-19 infection affects the lungs. Analysis of CT scans is very significant, especially when physicians are striving for quick solutions. This study successfully segmented lung infection due to COVID-19 and provided a physician with a quantitative analysis of the condition. COVID-19 lesions often occur near and over parenchyma walls, which are denser and exhibit lower contrast than the tissues outside the parenchyma. We applied Adoptive Wallis and Gaussian filter alternatively to regulate the outlining of the lungs and lesions near the parenchyma. We proposed a context-aware conditional generative adversarial network (CGAN) with gradient penalty and spectral normalization for automatic segmentation of lungs and lesion segmentation. The proposed CGAN implements higher-order statistics when compared to traditional deep-learning models. The proposed CGAN produced promising results for lung segmentation. Similarly, CGAN has shown outstanding results for COVID-19 lesions segmentation with an accuracy of 99.91%, DSC of 92.91%, and AJC of 92.91%. Moreover, we achieved an accuracy of 99.87%, DSC of 96.77%, and AJC of 95.59% for lung segmentation. Additionally, the suggested network attained a sensitivity of 100%, 81.02%, 76.45%, and 99.01%, respectively, for critical, severe, moderate, and mild infection severity levels. The proposed model outperformed state-of-the-art techniques for the COVID-19 segmentation and detection cases. generative adversarial network (GAN); COVID-19; lung segmentation; deep learning Article  
Lung's Segmentation Using Context-Aware Regressive Conditional GAN  
[PERSON], [PERSON], A.I.S. analyzing and quantifying COVID-19. There are different types of lung segmentation methods proposed by researchers, which are divided into two categories, i.e., handcrafted and deep-learning methods. Handcrafted image segmentation methods such as morphological-based methods [4] and active counter model [5] need physician intervention, are biased, and are time-consuming. These processes are often carried out in a sequence of steps, where empirical parameter adjustments are made manually. Various 3D-based [6] and 2D-based techniques [4] have been devised to obtain the best quality results. Moreover, handcrafted segmentation methods are tailored to specific imaging modalities, applications, and even datasets. It is arduous to generalize various types of imaging modalities and applications. Therefore, automatic segmentation of lung infections due to COVID-19 is highly desired in healthcare settings. Earlier deep-learning models were based on bounding boxes [7]. The goal is to guess the central pixel(s) class label through the patch that includes its neighbors. In the field of medical image processing, numerous successful pixel-wise classification approaches based on deep learning have been designed. Ref. [8] developed a deep-learning model to segment breast and mammographic risk scoring density. Ref. [9] created a fully connected architecture for the semantic segmentation of images. Ref. [10] proposed a deep-learning-based architecture viz. segCaps for the segmentation of lung. They introduced the concept of the deconvolutional capsule in their model. Recently, a lot of efforts have been made to design a deep-learning-based model to assist rapid and precise diagnosis of COVID-19 through medical imaging [11; 12; 13]. For example, ref. [14] developed a deep-learning architecture viz. VB-Net to segment lung infection and lung lobes from CT images of a COVID-19 Patient. Ref. [15] proposed a CT-imaging-based multi-task deep-learning architecture for the segmentation of lung infections. Ref. [16] designed mining, a lightweight deep-learning model to overcome the problem of overfitting and high computation cost.