## 2 Related Work  
During the last two decades, great attention of researchers has been focused on automatic classification of medical images, and, to that end, a plethora of different techniques have been employed [29]. Although some interpretations are still made by a visual inspection of the obtained scans, Computer-Aided Detection or Computer-Aided Diagnosis (CAD) facilities help radiologists to detect lesions on chest X-rays [29]. However, in its early stage of usage, CAD can be defined solely as a computer analysis tool for image data. Over time, research efforts transformed CAD into automatic diagnostic tools [30]. The majority of the proposed approaches in the literature, principally devoted to the diagnosis of lung cancer [31], exploits low-level handcrafted features [32]. These approaches are founded on: texture-based features [33], edge-based features [34; 35], graph mining [36], and color-based features [37; 38; 39]. Specifically, these works exploit different methodologies to perform image segmentation in regions of interest for building up masking operations that label them as either infected or not infected lungs. In particular, such as the proposed approach, the works in Reference [40; 41; 42] exploit histogram-based information to characterize and/or classify medical images. In the past, histograms, due to the simplicity of their computation and accuracy of the produced results, have been widely used for object recognition [43; 44; 45; 46], identification [47], and/or mining from images [27; 28]. Among the applications in medical images, Ref. [41; 42] used distance-based approach in detecting anomalies in images. Specifically, Reference [41] evaluates the histogram of a 3D volume of neuronal cells and fit them with a Gaussian function, in order to define different clusters in the images. Similar to the approach proposed in this paper, Reference [42] performs automatic segmentation of cell nuclei by exploiting an adaptive (local) threshold computed on the basis of the mean and standard deviation of the pixelgray-level values. However, unlike the proposed approach, these works do not directly employ histograms to detect images of infected patients. Many works have been focused on important classes of medical images, i.e., CXRs and CT scans. These studies analyze such images to detect tuberculosis [48; 49; 50] and lung cancer [51; 52; 53; 54]. However, CXRs and CT scans can be useful also to detect other important diseases, such as viral or bacterial pneumonia [55; 56]. In this regard, CXRs and CT scans have been massively used for the detection of COVID-19 disease. Although the pandemic is quite recent, several contributions are available in the literature. However, even if there are some sparse works about the manual screening of such images [57; 58; 59; 60; 61], almost all of the state-of-the-art approaches relate to the use of Machine Learning (ML) [62] and Deep Learning (DL) [63; 64] techniques. The ML, in fact, could become a helpful and potential powerful tool for large-scale COVID-19 screening [13; 65], and the author in Reference [3] has recently hypothesized that DL techniques applied to CT scans can become the first alternative screening test to the rRT-PCR in the near future. Motivated by this expectation, in the last year, the DL has been successfully used for CXRs [15; 20; 66; 67], CT scans [56; 68; 69; 70], or both [18; 71]. Being, indeed, challenging to summarize all the available literature in a single paper, there are some useful reviews regarding the application of DL techniques to COVID-19 detection on CXRs [72], CT scans [73; 74], and both [65; 75]. A systematic review on the detection of COVID-19 using chest radiographs and CT scans, highlighting strongness and weakness of several different approaches, can be found in Reference [76]. There are also some surveys of related datasets available [77; 78].