# Lung's Segmentation Using Context-Aware Regressive Conditional GAN
## 2 Related Work  
Recognition of COVID-19 from other kinds of pneumonia is very challenging compared to other lung problems due to high intra-class similarities (particularly in early stages) and low inter-class variations. Deep learning algorithms have a surge in popularity due to standout performance in medical image analysis. Recently, numerous studies have been conducted based on machine learning for the diagnosis of COVID-19 through CT images [11; 12; 13] and several of these representative studies are summarized in this article. Prior research studies focused on two classes, i.e., multi-class and binary classification. Multi-class identification detected COVID-19 cases from pneumonia cases, other lung problems, and normal cases. Researchers made strenuous efforts to classify COVID-19 cases from non-COVID-19 issues in binary classification. In this research, we aim to classify COVID-19 patients from non-COVID-19 patients. [PERSON] et al. [17] implemented AH-Net and several other state-of-the-art deep-learning models to locate lung parenchyma, subsequently recognizing COVID-19 infection from CT images. They used a diverse dataset consisting of 2587 lung infection CT scans. The dataset consists of 1695 non-COVID-19 cases and 922 COVID-19 cases. AH-Net achieved a segmentation accuracy of 95%, while 3D-Densnet-121 was employed to identify COVID-19 and non-COVID-19 infection and achieved a discriminative accuracy of 88.9%. [PERSON] et al. [18] designed a lesion identification framework to quantify the area affected by COVID-19. They used lung CT images. Three separate 2-D UNet were implemented for X-Y, X-Z, and Y-Z views of the CT image, each with five neighboring slices as an input and a central portion as an infection prediction mask. The proposed model achieved a sensitivity of 77.6% and a DSC score of 78.3%. [PERSON] et al. [19] developed a semi-supervised deep-learning-based model for the localization and classification of COVID-19. Firstly, the segmentation of the infectious regions was carried out through the help of a pre-trained UNet. Secondly, a deep CNN was employed to classify the lung region to COVID-19 or not. They used a dataset consisting of 313 images of COVID-19 and 229 images of non-COVID-19 cases. They obtained a classification accuracy of 0.95% and a 0.976% precision score. However, 68.5% of HR (hit rate) recorded a low segmentation result. [PERSON] et al. [20] designed a joint framework to integrate the clinical findings such as exposure history, symptoms, and lab tests with CT images to classify COVID-19 and non-COVID-19 cases. They trained a joint framework with the help of a dataset consisting of 905 cases. They achieved a specificity of 82.8%, sensitivity of 84.3%, and 0.92% AUC (area under the curve) values. [PERSON] et al. [21] developed a model for the segmentation and quantification of aberrant patterns on lung CT scans of COVID-19 cases. They used 9749 lung CT scans, segmented lesions, and lobe regions. They employed different evaluation matrices to quantify severity: % of opacity, % of high opacity, severity score, etc. [PERSON] et al. [22] developed an automatic approach to measure COVID-19 pneumonia progression and severity. UNet was employed to segment lungs and vessels through 120 CT images of COVID-19 patients. For the segmentation of lesions, the proposed method obtained a DSC score of 81% and 95% for the segmentation of lung regions. [PERSON] et al. [23] designed a framework to determine the severity of COVID-19 pneumonia. The developed framework consists of four stages, i.e., pulmonary vessels segmentation, lung segmentation, and lobes segmentation and infection detection. The segmentation was performed using adaptive region growing and thresholds. The Pearson correlation between physician and computer ranged from 0.8373 to 0.7679. [PERSON] et al. [24] created a hybrid classification model that combines an improved PSO (particle swarm optimization) and a deep CNN model. The aim was to achieve high performance with minimum resource utilization and storage capacity.