## 2 Related Works  
Recently, medical imaging processing techniques have been widely used to monitor several diseases. The progress in this field has been reinforced by the introduction of Artificial Intelligence technologies that became a popular approach for detection and segmentation of many medical problems thanks to their powerful feature representation [15]. In this context, many approaches have been proposed for the detection and the segmentation of the lungs' COVID-19 infection using chest X-rays and CT scans in the last few months [16], which confirmed that a carefully designed image examination procedure plays a vital role in reducing the diagnostic burden. The proposed methods can be classified into three categories: **(1)** classification techniques; **(2)** infected regions and segmentation techniques and; **(3)** diagnosis systems that worked on both tasks. Table 1 presents two proposed methods for each category as well as the deep learning architectures used in each one of them and the images' modality they used. [PERSON] et al. [17] introduced COVID-Net, a densely-connected deep convolutional neural network design, tailored for the detection of COVID-19 cases from chest X-ray images, and it achieved a 93.3% test accuracy. A three-phase detection model using deep transfer learning is proposed, in the work of [PERSON] et al. [19] to improve the detection accuracy, and it was proven that the ResNet18 architecture helped to attain a better classification accuracy (99.4%) compared with the other considered architectures. [PERSON] et al. [20] developed a new Deep Network called \"Inf-Net\" to automatically identify infected regions from chest CT slices. The algorithm is based on a parallel partial decoder able to aggregate the high-level features and to generate a global map. In their study, they used a small dataset of 100 CT labeled images which achieved a dice score of 0.682. A semi-supervised segmentation system was then introduced to alleviate the shortage of labeled data and it achieved a dice score of 0.739. Similarly, [PERSON] et al. [21] proposed a deep learning based system for automatic segmentation and quantification of infection regions from chest CT scans. A modified 3D CNN that combines V-Net [25] with the bottle-neck structure was used and they achieved a Dice coefficient of 91.6%. Another study developed by [PERSON] et al. [22] who proposed a novel approach that combines CNN and VGG19 to detect COVID-19 features in chest X-ray images. In their study, they used a dataset of eighty-seven chest X-ray images associated with twenty-five cases and they obtained an accuracy of 96.93%, a sensitivity of 57.14%, and a specificity of 99.2%. A joint classification and segmentation system was proposed by [PERSON] et al. [23] for a real-time diagnosis of COVID-19. The classification model is a Res2 Net-based [26] classifier that achieved an average sensitivity of 95.0% and a specificity of 93.0%. The segmentation model is based on an encoder, with VGG-16 backbone [27] added with an Enhanced Feature Module, and a decoder based on Attentive Feature Fusion strategy. The model succeeded to segment the infected regions with a dice score of 78.3.