# A Soft-Voting Ensemble Classifier for Detecting Patients Affected by COVID-19
## 1 Introduction  
COVID-19 is an ongoing global pandemic of the coronavirus disease 2019 caused by severe acute respiratory syndrome. First identified in December 2019 in Wuhan, China, the virus has spread quickly to all continents (at the time of writing, the World Health Organization is reporting around 481 million confirmed cases of COVID-19, which have caused over 6 million deaths). Reverse Transcriptase--Polymerase Chain Reaction (RT-PCR) and antigen tests are the main diagnostic tests for COVID-19. During the pandemic, RT-PCR has shown its limitations in detecting COVID-19. On the one hand, the performance of this diagnostic test reported a low sensitivity, resulting in a large number of false negatives. On the other hand, RT-PCR is a laboratory technique that requires several hours to provide a diagnostic result. Unlike RT-PCR, antigen tests provide quick diagnoses, but they have a lower sensitivity with respect to that of RT-PCR [1]. A fast and accurate diagnostic test would allow both to quickly isolate infected patients limiting the spread of the virus and to promptly provide them with the first targeted treatments. Diagnostic imaging is an alternative approach aimed at detecting COVID-19 infections. In particular, chest scans obtained with computed tomography (CT) allow accurately detecting lung abnormalities due to COVID-19, together with the possibility of identifying lesions that explain the degree of severity of the disease. Unfortunately, a diagnosis can only be made by analyzing a multitude of chest slices obtained through a scan. Moreover, recognizing lung infections related to COVID-19 is not trivial. Although the CT findings of COVID-19 are well known [2], it can be difficult to properly differentiate COVID-19 from other kinds of viral pneumonia. A targeted study involving radiologists in China and the United States of America showed how their performance was characterized by a moderate sensitivity in distinguishing COVID-19 from viral pneumonia [3]. Therefore, it is essential to support radiologists with automated tools able to analyze CT scans and targeted to provide accurate COVID-19 diagnosis in a short amount of time. Deep learning (DL) can be successfully used to implement algorithms able to deal with this challenge. DL has rapidly become a methodology of choice in diagnostic imaging [4]. It is increasingly used to analyze medical images in different fields obtained with different medical imaging tools (e.g., [5; 6; 7]) including CT (e.g., [8; 9]). Some DL-based solutions have also been proposed to detect COVID-19 lung infections. With no claim to be exhaustive, some works are reported hereinafter. For the screening of COVID-19, [PERSON] et al. [10] proposed a method based on EfficientNet [11] that adopts transfer learning and a voting-based approach. In particular, the 2D images of a chest CT scan from a given patient are classified as a group in a voting system. In [12], the authors proposed CovNet19 to predict COVID-19 by analyzing chest X-ray images. CovNet19 uses pre-trained Convolutional Neural Networks (CNNs) for feature extraction and a Support Vector Machine (SVM) [13] for classification. In [14], [PERSON] et al. proposed a system consisting of a cascade of two models: a lung-lesion segmentation model and a diagnostic model. The former is used to generate a lung-lesion map, which is given as input to the latter. The diagnostic model classifies scans as related to novel coronavirus pneumonia, common pneumonia and normal controls. A similar approach aimed at classifying COVID-19, SARS, and MERS chest X-ray (CXR) images using CNNs is presented in [15]. A unique dataset representative of the three infections was compiled and used as a benchmark for the study. Then, a two-level recognition system was implemented. In the first step, the lung regions are segmented, while in the second step, a CNN is used to classify the segmented regions. In [16], the authors proposed a system to perform lung segmentation, COVID-19 diagnosis, and COVID-19 infections slices locating.