# Deep Learning for COVID-19 Diagnosis from CT Images
Furthermore, they achieved optimal results in many domains, such as computer vision devoted to medical analysis, with images coming from magnetic resonance imaging (MRI) [27], microscopy [28], CT [29], ultrasound [30], X-ray [31], and mammography [32]. They have been successfully applied to various different problems, like classification or segmentation [33; 34; 35; 36]. Deep learning-based methods have also made significant progress in the analysis of lung diseases, which is a comparable scenario to COVID-19 [37; 38; 39]. However, the scenario of CT images of lungs referred to COVID-19 and non-COVID-19 patients can be particularly problematic to classify, especially when the damage due to pneumonia of different causes is present simultaneously. The main findings of chest CT scans of COVID-19 positive patients indicate traces of ground-glass opacity (GGO) [40]. Two CT scans of COVID-19 and non-COVID-19 are shown in Figure 1.  
The overall objective of this study is to investigate the behaviour of the main existing off-the-shelf CNNs for the classification of patients' CTs. This work is a preliminary investigation for the future development of a tool that provides confirmation of the viral test result or provides more details about the ongoing infection, also considering that according to the Center for Disease Control (CDC), even if a chest CT or X-ray suggests COVID-19, the viral test is the only specific method for diagnosis [42]. Specifically, we propose a comprehensive investigation of the problem of COVID-19 classification from chest CT images from different perspectives:  
1. We present a comparative study of several off-the-shelf CNN architectures in order to select a suitable deep learning model to perform a three-class classification on the public COVIDx CT-2A dataset, specifically divided into COVID-19, pneumonia and healthy cases;
2. On the same dataset, we performed a patient-oriented experiment by grouping all the CT images of the patients, in which the aim was to provide a diagnosis;
3. We investigated the robustness of the methods by performing two cross-dataset experiments and evaluating the performance of CNNs previously trained on COVIDx CT-2A. In particular, we performed a two-class classification between COVID-19 and healthy cases, on the COVID-CT dataset, without fine-tuning;
4. We repeated the experiment just described, by fine-tuning the most promising CNNs, demonstrating that it is still problematic to integrate automatic methods in the clinical diagnosis of COVID-19. We both demonstrate how off-the-shelf deep learning architectures can be utilised to classify CT images representing COVID-19 affected patients and how transfer learning capabilities are still far from offering a concrete contribution in a real-world scenario, as demonstrated by our cross-dataset experiments, without addressing it with different techniques. The experiments are not intended to provide an exhaustive comparison of the performance of these methods; instead, we wanted to select the most suitable for our classification of CT images without, for the time being, investigating possible parametric improvements. The purpose is to create a concrete baseline with the potential to be modified and developed further. Moreover, several works in the context of COVID-19 diagnostics have considered small or private datasets or lacked rigorous experimental methods, potentially leading to over-fitting and overestimation of performance [7; 43]. For this reason, we:  
1. Carefully selected the two datasets on which to conduct the experiments described. In fact, [PERSON] et al. [7] have recently shown that most of the datasets used in the literature for the diagnosis or prognosis of COVID-19 suffer from duplication and quality problems;
2. Selected COVIDx CT-2A, a public reference dataset specifically proposed for COVID-19 detection from CT imaging, because of the high risks of bias due to source problems and datasets created from unsupervised public online repositories.