# Deep Learning for COVID-19 Diagnosis from CT Images
### Related Work  
Here, we briefly describe some works that have addressed tasks related to COVID-19. Although the research is still evolving, the automatic classification of COVID-19 has gained wide attention from researchers around the world [7; 42; 44; 45]. In this context, we can broadly distinguish the proposed methods into those based on 2D and 3D images. Among the most recent ones [46; 47], 3D images could be handy to avoid losing the interstitial information of the lungs. However, several works have exploited 2D images showing the property of extracting representative features of COVID-19 lesions for disease detection [48; 49; 50; 51; 52; 53; 54; 55; 56]. They are all CNN-based and used CT [48; 49; 50; 51; 52; 53; 54; 55] or CXR [43; 50; 56] images. We particularly focused this study on deep learning-based classification methods for COVID-19 detection. Among the CT-based methods, [PERSON] et al. [48] proposed a deep learning-based system for COVID-19 diagnosis, performing lung segmentation, COVID-19 diagnosis, and COVID-infectious slices location. In contrast, [PERSON] et al. [51] proposed a weakly supervised multiscale deep learning framework for COVID-19 detection, inspired by the VGG architecture [57], which assimilates different scales of lesion information using CT data of the chest. [PERSON] et al. [52] implemented a lightweight CNN, based on the SqueezeNet model [58] for efficient discrimination of COVID-19 CT images against other community-acquired pneumonia or healthy CT images. [PERSON] et al. [53] used a transfer learning strategy on the three pretrained models of VGG-16 [57]. ResNet50 [59], and Xception [60], combining them with the ensemble stacking strategy and tested the method on CT images of the chest. [PERSON] et al. [55] adopted the ResNet-v2, a modified version of ResNet [59]. Moreover, they added group normalisation instead of batch normalisation and conducted a weight standardisation for all convolutional layers. Lastly, they also incorporated the pre-training data from CIFAR-10 [61], ILSVRC-2012 [62], and ImageNet-21k [63] as the parameters for initialisation. On the subject of CXR-based works, [PERSON] et al. [50] employed four pretrained models (ResNet18 [59], ResNet50 [59], SqueezeNet [58] and DenseNet-121 [64]) on CXR data, and analysed their performance for COVID-19 detection. On the other hand, [PERSON] et al. [43] developed BS-Net, a multi-block deep learning-based architecture designed for the assessment of pneumonia severity on CXRs. More recently, [PERSON] et al. [56] proposed CovFrameNet, a novel deep learning-based framework based on a substantial image pre-processing step and a CNN architecture for detecting the presence of COVID-19 on CXRs. Thanks to the powerful discriminative ability of CNNs, several authors tried to propose CNN-based frameworks for the diagnosis or prognosis of COVID-19, even though CNNs typically require large scale datasets to perform a correct classification. However, most of the existing CT scan datasets for COVID-19 contain at most hundreds of CT images[65; 66; 67]. Therefore, we exploited COVIDx CT-2A [68], composed of 194,922 CT images, as described in Section 2.1.1 to propose a baseline classification approach and we evaluated it on the external dataset COVID-CT, described in Section 2.1.2 to assess generalisability of the proposal. In general, we aim to avoid the following drawbacks:  
1. Using small scale datasets;Using not robust or multiple unsupervised source datasets;
3. Testing the method without external validation. Regarding the works that employed the datasets used in our study, [PERSON] et al. [41] worked on COVID-CT, while [PERSON] et al. [54] on COVIDx CT-2A. The former is based on a transfer learning approach on the DenseNet network, while the latter proposed COVID-Net CT [54], a deep convolutional neural network tailored for detection of COVID-19 cases from chest CT images. This work differs from those described above because:  
1.