## 2 Related Work
An overview of these works points out that DL approaches, mainly based on the supervised paradigm, can be divided into two main families: those based on segmentation and those that perform the classification task directly. The approaches, which are based on segmentation, are usually founded on U-Net type architecture to identify relevant part of the CXRs/CT scans and perform classification, focusing the attention only on these sections [56; 79; 80; 81; 82; 83; 84]. The second family of approaches, instead, is based on the binary classification problem of COVID/Non-COVID images [20; 69; 70; 85; 86; 87; 88] and utilize deep Convolutional Neural Networks (CNNs) and their variants, including VGG16, InceptionV3, ResNet, and DenseNet. Finally, we point out that there are a few approaches that exploit the unsupervised paradigm, such as the use of deep auto-encoders [89; 90; 91; 92]. Specifically, these works rely on deep or stacked autoencoders to automatically extract a set of meaningful features and then use softmax classifiers on the top to distinguish images of infected lungs from the healthy ones. Among DL approaches, there exist some papers that exploit image histograms [93; 94; 95; 96; 97; 98; 99]. Specifically, the work in Reference [93], after a pre-processing with a median filter, extracts meaningful features from cropped regions by using Histogram Orientation Gradients (HOGs) and uses a final classification stage with a feed-forward neural network. Reference [94] introduces a pre-processing step based on the Contrast Limited Adaptive Histogram Equalization (CLAHE) idea to improve the contrast and generate an enhanced CXR image. Authors in Reference [95] propose a statistical histogram-based method for the pre-categorization of skin lesion: the image histogram is used to check the image contrast variations and classify these images into high and low contrast images. Similarly, Reference [98] uses histogram thresholding techniques to perform image clustering and then propose an image segmentation method for skin lesion delineation. Authors in Reference [96] propose ImHistNet, a deep neural network for end-to-end texture-based image classification based on a variant of the learnable histogram introduced in Reference [100] for pattern detection in pictures. The work in Reference [97], similar to our approach, extracts features from suitable inter-histogram distances but uses these features by feeding them into several (supervised) classifiers. Similarly, Reference [99] uses histogram to extract meaningful features, along with other statistical characterizations, and then performs a final classification through an artificial neural network. Moreover, the works in Reference [101; 102] exploit histogram-based enhanced techniques and metaheuristic approaches in Magnetic Resonance Imaging (MRI). Specifically, Reference [101] introduces MedGA, an image enhancement method based on Genetic Algorithms (GAs) that improves the threshold selection for the segmentation of the region of interest. MedGA uses a pre-processing stage for the enhancement of MR images based on some nonlinear histogram equalization techniques. Similarly, the work in Reference [102] proposes a Particle Swarm Optimized (PSO) texture-based histogram equalization technique to enhance the contrast of MRI brain images and find an optimal threshold for the segmentation. The obtained results overcome those of standard histogram equalization and those presented in Reference [101]. Finally, also the recent work in Reference [103] exploits DL segmentation-based histogram and threshold analysis in chest CT scans for differentiating healthy lungs from the infected ones. Specifically, the proposed method automatically derives imaging biomarkers from chest CT images based on histogram and provides a Hounsfield unit (HU) threshold analysis to assess differences in those biomarkers between lung-healthy individuals and those affected by atypical pneumonia.