# Deep Learning for COVID-19 Diagnosis from CT Images  
[PERSON]  
1 Department of Mathematics and Computer Science, University of Cagliari, Via Ospedale 72, 09124 Cagliari, Italy; [EMAIL_ADDRESS] (F.P. ); [EMAIL_ADDRESS] (C.D.R. )1  
[PERSON] and [PERSON]  
1 Department of Mathematics and Computer Science, University of Cagliari, Via Ospedale 72, 09124 Cagliari, Italy; [EMAIL_ADDRESS] (F.P. ); [EMAIL_ADDRESS] (C.D.R. )1  
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Therefore, there is a need for faster and more reliable screening techniques that could further confirm the PCR test or replace it entirely, such as imaging-based methods. They may complement its use to achieve greater diagnostic certainty or even substitute in some countries where RT-PCR is not readily available. In some cases, chest X-ray (CXR) abnormalities are seen in patients who initially had a negative RT-PCR test, and several studies have shown that chest computed tomography (CT) has greater sensitivity for COVID-19 than RT-PCR and could be considered a primary tool for diagnosis [3; 4; 5; 6]. In response to the pandemic, researchers have rushed to develop models using artificial intelligence (AI), particularly machine learning, to support clinicians [7]. Computed tomography is already a widely explored medical imaging technique that allows non-invasive visualisation of the interior of an object [8; 9; 10; 11; 12; 13] and is widely used in many applications, such as medical imaging for clinical purposes [14; 15; 16; 17; 18]. For this reason, clinical institutions have used CT as an effective and complementary screening tool alongside RT-PCR [5; 6] with a higher sensitivity of up to 98% compared to 71% for RT-PCR [19; 20]. In particular, several studies have shown that CT has excellent utility in detecting COVID-19 infections during routine CT examinations for reasons unrelated to COVID-19, such as monitoring of elective surgical procedures and neurological examinations [21]. Other scenarios where CT imaging has been exploited include cases where patients have worsening respiratory complications and cases where patients with negative RT-PCR test results are suspected to be COVID-19 positive due to other factors. Early studies have shown that chest CT images of patients may contain some potential indicators for COVID-19 [2; 5; 6; 22] infections, but may also be contained in non-COVID-19 infections. This issue can lead to challenges for radiologists in distinguishing COVID-19 infections from non-COVID-19 infections using chest CT [23; 24]. However, the duration of diagnosis is the main limitation of CT scan tests: even experienced radiologists need about 21.5 min to analyse the test results of each case [25], and during the emergency, a large number of CT images have to be evaluated in a very short time, thus increasing the probability of misclassification. For this reason, intelligent automatic diagnosis systems that automatically classify chest CT images can help to improve speed and to rapidly confirm the test result. In recent years, deep learning workflows have emerged from the proposed AlexNet convolutional neural network (CNN) in 2012 [26]. CNNs do not follow the typical workflow of image analysis because they can extract features independently without the need for feature descriptors or specific feature extraction techniques. Therefore, they differ from conventional machine learning methods because they require little or no image pre-processing and can automatically infer an optimal data representation from raw images without requiring prior feature selection, resulting in a more objective and less biased process.